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Bearings are widely used as a common mechanical component and significantly impacts the reliability and safety of various engineering equipment. In the field of large‐scale equipment such as wind turbines, statistical data for reliability and state prediction of bearings is usually limited, leading to the inapplicability of traditional statistical methods. Besides, individual differences commonly exist among bearings, and the appropriate failure threshold for a specific bearing is difficult to be determined due to the individual uncertainty, which may induce prediction errors. To address these issues, a novel operational reliability and state prediction method based on random threshold degradation hidden Markov model was proposed in the study. The traditional hidden Markov model was improved by considering the effect of performance degradation on state transition probability matrix. Moreover, random failure thresholds of performance degradation were employed to describe individual differences between bearings. The operational reliability and state of bearings was obtained by comprehensive analysis of prediction results under different failure thresholds. Verification of the proposed operational reliability and state prediction method were conducted using PHM2012 bearing operation data. The results underscored the effectiveness of the proposed method in achieving a comparatively high accuracy in operational reliability prediction without the prerequisite of an exact failure threshold, when contrasted with several established reliability and life prediction techniques. [ABSTRACT FROM AUTHOR]

Predicting the remaining useful life (RUL) of bearings is crucial for maintaining the reliability and availability of mechanical systems. Constructing health indicators (HIs) is a fundamental step in the methodology for predicting the RUL of rolling bearings. Traditional HI construction often involves determining the degradation stage of the bearing by extracting time–frequency domain features from raw data using a priori knowledge and setting artificial thresholds; this approach does not fully utilize the vibration information in the bearing data. In order to address the above problems, this paper proposes an Asymmetric Residual Shrinkage Convolutional Autoencoder (ARSCAE) model. The asymmetric structure of the ARSCAE model is characterized by the soft thresholding of signal features in the encoder part to achieve noise reduction. The decoder part consists of convolutional and pooling layers for data reconstruction. This model can directly construct HIs from the original vibration signals collected, and comparisons with other models show that it constructs better HIs from the original vibration signals. Finally, experiments on the FEMTO dataset show that the results indicate that the HIS constructed by the ARSCAE model has better lifetime prediction capability compared to other methods. [ABSTRACT FROM AUTHOR]

Owing to their high specific energy capabilities, ultramicrogas turbines (UMGT) are a high-potential technology to provide portable electric power supply for applications with demand of less than 1 kW. UMGT conceptual design is challenged by small-scale effects augmenting interdisciplinary dependencies leading to highly coupled, nonlinear component interactions. This work provides a novel approach to conceptual UMGT design by combining reduced order component and system modeling with constrained multi-objective optimization. Hereby, Part I presents integrated design and performance modeling of compressor, turbine, combustor, and generator. In Part II, the heat engine and generator modules are merged into a system framework by establishing conceptual UMGT rotor geometry and engine design. Following bearing selection and lifetime assessment, experimentally validated reduced order models are developed for heat transfer and rotordynamic analysis. Using the elaborated framework, a constrained multi-objective system optimization of a 300 W engine is performed based on ten design parameters and comparing SiAlON and Inconel 718 as potential rotor materials available for additive manufacturing. Hereby, bearing lifetime, system efficiency, and specific power are maximized while meeting rotordynamic, structural, and thermal requirements. Evaluating the results, interdisciplinary effects are highlighted, and two optimum engine configurations are suggested. [ABSTRACT FROM AUTHOR]

Faults in rotating systems can cause significant damage to the machinery and can result in downtime and production losses. Hence, the timely detection and diagnosis of faults are very important for the smooth running of machines and the assurance of their safety and reliability. In view of this, a review of the literature has been presented in the article on the types of additive faults and their identification using conventional signal-based techniques and automated artificial intelligence techniques. Through a literature survey, the faulty rigid and flexible rotor systems mounted on rolling element bearings, hydrodynamic bearings, and active magnetic bearings have been studied. The faults incorporated in this article are the additive fault types, in which the process is affected by adding process variables. The rotor unbalances, shaft or bearing misalignment, crack, internal damping, bow in the shaft, rotor-to-stator rub, and mechanical looseness are the classifications of additive faults. Additionally, understanding the rotor response through theoretical and experimental investigations influenced by the additive faults and its detection and diagnosis using vibration and current-induced signals is extremely important, and therefore the present paper briefly discusses this. Following the state of the art in the dynamic analysis and identification of multiple hazardous faults, the general remarks and future directions for further research have been suggested at the end of this article. [ABSTRACT FROM AUTHOR]

This study investigates the capability of WC/C coatings to protect bearing bores against fretting wear when there is a lack of lubrication, a typical failure mechanism in bearings subjected to vibrations. Linear pin-on-disc and rotary oscillating block-on-ring tribological tests were carried out reproducing fretting conditions to evaluate the tribological performance and fretting resistance of the developed WC/C coating in comparison with those of the two common coatings currently employed to prevent this type of bearing failure, i.e. thin-dense chrome and fluoropolymer coatings. The friction forces generated under fretting conditions were evaluated, and a deep surface analysis of the tribosystems was carried out through different microscopic techniques in order to identify the wear mechanisms prevailing in each coating. Results suggested that WC/C coatings could be promising candidates to mitigate fretting damage, as they combine a low friction coefficient with good wear resistance. Furthermore, WC/C coatings constitute a more sustainable solution compared to the currently employed PTFE and chrome-based coatings, paving the way to a greener society. [ABSTRACT FROM AUTHOR]

Global issues such as climate change, environmental pollution, and conservation of resources require manufacturers of internal combustion engines (ICE) to achieve substantially reduced emissions of greenhouse gases and pollutants as well as increased engine efficiency and durability. Condition monitoring and predictive maintenance approaches for sliding bearings in ICEs are key tools for increasing engine durability and saving resources by exploiting more of the useful lifetime of a bearing while avoiding critical engine operation caused by bearing wear and failure. These approaches require appropriate measurement technology capable of acquiring informative parameters that reflect the current condition of the bearings while withstanding the high temperatures and mechanical loads encountered inside the engine and accommodating spatial constraints. This paper deals with research work related to advanced sensor technology that is highly integrated into sliding bearings so that information is obtained nearly directly from relevant areas such as the bearing running layer and the lubrication gap. An isolated, sputtered sensor layer with a thickness of a few micrometers is employed in combination with a laser structuring process to form the desired thin film sensor structure below the bearing running surface. While several measurement parameters and corresponding sensor types are conceivable, this paper focuses on temperature and strain measurements that rely on a change in the electrical resistance of the sensor layer material. Promising sensor layouts and positions targeted for use in condition monitoring applications in ICEs are elaborated in detail. Developments and challenges in implementing the sensor technology concept--in particular with regard to the process of manufacturing the sensor as well as the wire contacting--are outlined in depth. The paper concludes by presenting measurement results obtained with this sensor technology at lab scale as well as an outlook toward implementing the instrumented bearings in ICEs. [ABSTRACT FROM AUTHOR]

In mechanical equipment, if bearing components fail, it can cause serious equipment damage and even threaten human life safety. Therefore, equipment bearings fault diagnosis is of great meaning. In the study of bearing fault diagnosis, an improved gray wolf optimization algorithm is put forward to optimize the support vector machine model. The model improves the convergence factor of the algorithm, and then optimizes the penalty factor and KF parameters of the support vector machine to enhance the accuracy of fault classification. At the same time, in the problem of fault identification, the introduction of adaptive noise set empirical mode decomposition and the combination of permutation entropy are studied to minimize the impact of noise on the identification model. The experimental outcomes indicated that the algorithm proposed in the study had an average fitness value and a standard deviation fitness value of 0 in the benchmark test function and 94.55% accuracy in overall fault identification. The permutation entropy of the vibration signal in the normal state of the bearing was within the range of [0.13, 0.52], which has a more stable state compared to the fault state. The results show that the improved grey Wolf optimization algorithm is applied to the optimization of support vector machine, combined with the adaptive noise set empirical mode decomposition and permutation entropy, and the identification and classification results of bearing faults are successfully improved, making the proposed method feasible in bearing fault diagnosis, and providing a more effective scheme for fault diagnosis. [ABSTRACT FROM AUTHOR]

The advent of Industry 4.0 has ushered in a new era of technological advancements, particularly in integrating information technology with physical devices. This convergence has given rise to smart devices and the Internet of Things (IoT), revolutionizing industrial processes. However, despite the push towards predictive maintenance, there still is a significant gap in fault prediction algorithms for electrical machines. This paper proposes a signal spectrum-based machine learning approach for fault prediction, specifically focusing on bearing faults. This study compares the effectiveness of traditional neural network algorithms with a novel approach integrating fuzzy logic. Through extensive experimentation and analysis of vibration spectra from various mechanical faults in bearings, it is demonstrated that the fuzzy-neuro network model outperforms traditional neural networks, achieving a validation accuracy of 99.40% compared to 94.34%. Incorporating fuzzy logic within the neural network framework offers advantages in handling complex fault combinations, showing promise for applications requiring higher accuracy in fault detection. While initial results are encouraging, further validation with more complex fault scenarios and additional fuzzy layers is recommended to fully explore the potential of fuzzy-neuro networks in fault prediction for electrical machines. [ABSTRACT FROM AUTHOR]

We present a novel multi-modal adaptive feature extraction algorithm considering both time-domain and frequency-domain modalities (AFETF), coupled with a Bidirectional Long Short-Term Memory (Bi-LSTM) network based on the Grey Wolf Optimizer (GWO) for early-stage weak fault diagnosis in bearings. Singular Value Decomposition (SVD) was employed for noise reduction, while Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was utilized for signal decomposition, facilitating further signal processing. AFETF algorithm proposed in this paper was employed to extract weak fault features. The adaptive diagnostic process was further enhanced using Bi-LSTM network optimized with GWO, ensuring objectivity in the hyperparameter optimization. The proposed method was validated for datasets containing weak faults with a 0.2 mm crack and strong faults with a 0.4 mm crack, demonstrating its effectiveness in early-stage fault detection. [ABSTRACT FROM AUTHOR]

This study aims to verify the effectiveness of the 250 HP class compressor system to which the integrated hybrid air-foil magnetic thrust bearing (i-HFMTB) proposed in (Ha et al., 2023, "Integrated Hybrid Air Foil-Magnetic Thrust Bearing (i-HFMTB) Part I: Preliminary Experimental Test for Rotordynamic Parameter Identification and Behavior With PD Control," ASME Paper No. GT2023-102860) [1] is applied, and the vibration and instability problems of air-foil journal bearing (AFJB) occurring in the rigid mode are controlled by i-HFMTB. The compressor rotor is supported by two AFJB (journal diameter = 60 mm) and an i-HFMTB, and the length and mass of the rotor are 550 mm and 15.24 kg, respectively. i-HFMTB has a structure in which air-foil thrust bearing (AFTB) pads are inserted into eight slotted active magnetic thrust bearings (AMTB), and PD control (proportional gain: 4000; differential gain: 10) is applied. The operating area was identified through AFJB's dynamic coefficients and rotordynamic analysis. As a result, in the experiment with the 250 HP compressor system supported only by two AFJB, sub- and super-synchronous vibrations were generated owing to the AFJB's insufficient load capacity and damping in the rigid mode (7000 rpm) region, and this instability did not disappear even upon increasing the speed to 15,000 rpm. However, when i-HFMTB was turned on in rigid mode, it was confirmed that the sub- and super-synchronous vibrations were significantly reduced. The thrust collar tilt angle was calculated through orbit trajectory analysis of the impeller and cooling side, and it was confirmed that the tilt angle of the thrust collar was reduced during i-HFMTB operation. [ABSTRACT FROM AUTHOR]

Means to decrease the energy consumption of tilting pad journal bearings (TPJBs) without affecting their performance and structural integrity are mandatory in a cost efficient operation. Reducing the lubricant flow supplied to a bearing is a distinct method to diminish drag power losses along with savings in oil storage and pump equipment. However, a too low flow remains questionable in industrial practice. Starved flow conditions produce hot pad surfaces that could lead to Babbitt failure; and under certain loads, generate subsynchronous vibrations (SSV). This paper aims to resolve some of the issues above via measurements of the load performance conducted with a four-pad TPJB configured as load-on-pad (LOP) and having its ends sealed or open to make flooded and evacuated conditions. Prior measurements were conducted with the same bearing under load-between-pads (LBP); see Refs. [1] and [2]. The nominal supplied flow (Q) of ISO VG 46 oil at 60 °C is proportional to shaft speed (max. 12 krpm: 62.8 m/s surface speed). In the tests, the flow Qs ranges from 1.5 Q to just ¼ Q, and the applied units load reaches 2.07 MPa. Compared to the flooded bearing, the evacuated bearing produces a slightly larger eccentricity across the range of flow rates. For a unit load = 2.07 MPa and shaft speed of 6 or 12 krpm, the highest pad subsurface temperature reaches ∼130 °C for Qs below 50% nominal. For both bearings, flooded or evacuated, drag power losses decrease to ∼30% as the oil flow drops from 100% to 50% Q. As the oil flow increases to 1.5 Q, the drag power increases ∼10% for both bearing types at 6 krpm and for the flooded one at 12 krpm, while the evacuated bearing shows a reduction of ∼7%. The drag power grows as the static load increases; the evacuated bearing producing up to ∼ 40% lesser power loss than the flooded bearing. Both bearings produce similar size direct stiffnesses though largely orthotropic, Kyy ≫ Kxx. Direct damping coefficients Cxx ~ Cyy increase with shaft speed and unit load but dramatically decrease as Qs drops, in particular for the evacuated bearing. The current measurements and those in Refs. [1] and [2] demonstrate that LOP and LBP bearings can safely operate with 50% of nominal flow thus saving drag power, and without too large pad, temperature rises. Alas, too low Qs produces a significant reduction in the damping coefficient orthogonal to the applied load direction. The effect is most evident in the LOP evacuated bearing, which is most prone to show SSV Hash. [ABSTRACT FROM AUTHOR]

In this paper, the authors present selected information on accelerated reliability tests of special off-road vehicles and their parts. The vehicles were tested in standard operation and on a polygon using accelerated durability tests of the bearings of the vehicles. The paper presents the theoretical basis of accelerated testing of the reliability properties of the parts and the possibilities of calculating the acceleration coefficient. The authors assumed the linearity of the acceleration function, which is proved in practice in most cases. The authors also present the possibility of using a special polygon for accelerated tests. [ABSTRACT FROM AUTHOR]

Starved lubrication occurs intentionally or unconsciously in many journal bearing applications. However, the determination of its concrete distribution in a journal bearing is rather complex as the different lube oil feed grooves interact with each other due to their connection by the lubricant flow along the pad surfaces and the periodic geometric property of the entire bearing. A robust numerical procedure to determine the lube oil distribution between the different feed grooves in a starved lubricated fixed-pad journal bearing is presented as an extension to Elrod's cavitation algorithm. The enhanced algorithm partly integrates flow rate boundary conditions into the linear system of equations for the solution of Reynolds equation and the transport equation. A strategy to couple these flow rate boundary conditions with the iterative procedure to determine the binary cavitation index is proposed. In particular, the algorithm enables an appropriate modeling of lube oil grooves with locked oil supply including the consideration of groove-specific dissipation or design features like bleed notches. The novel algorithm is implemented as a submodule of a thermo-elasto-hydrodynamic bearing code and validated with test data for a high-speed two-lobe bearing with embedded arc. Furthermore, a comprehensive parametric study provides an insight on the robustness of the implemented numeric procedure. Results, current limitations, and possible extensions to the algorithm are discussed. [ABSTRACT FROM AUTHOR]

A working prototype of a non-parallel shaft drive system inspired by the Ceramic Speed Driven system was developed. It consists of a driving wheel with bearings arranged coplanar to its outer diameter, which transmits power to a driven wheel with its axle positioned at 90 degrees to the driving axle. A graphical method obtained the tooth geometry on the driven wheel. Subsequently, a kinematic analysis was performed and it was found that the system behaves like a chain drive system, where the speed of the output shaft is not constant due to the polygonal effect. The driving and driven wheels were manufactured in resin by additive manufacturing and adapted to a workbench for mechanical testing. By measuring torque using a Prony brake, efficiency comparisons were made between the fabricated system and the spur and chain bevel gear drive systems. The proposed system was found to have similar efficiency to spur bevel gear drives and superior to chain drives. [ABSTRACT FROM AUTHOR]

The 1/3-binary tree structure bandwidth is fixed, while the bands containing fault information sometimes do not fall equally in the fixed structure. To solve this problem, it is proposed to utilize the power spectrum to replace the Fourier spectrum, and to reconstruct the signal based on the scale space representation for the empirical wavelet decomposition. The fluctuation characteristics of the power spectrum frequency components and the energy distribution within frequency bands are taken full advantage of in this method. A new size parameter updating mechanism is employed to accelerate the signal reconstruction process. At the same time, it is thought that fault frequency is affected by the factor of slippage phenomena in order to realize a self-running diagnosis of bearing failure, and the occurring probability of the bearing fault is introduced to transform the envelope spectrum into a scalar indicator. As a result, without human intervention, the whole failure diagnosis process of the rolling bearing can be achieved by the manner of nonparametric testing. Simulation signal analysis results and experimental analysis results demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Fault diagnosis methods for rolling bearings based on deep learning have become a research hotspot. However, these methods mostly use convolutional neural networks (CNNs), which have the problem of gradient dispersion or disappearance as the network deepens. Moreover, directly converting vibration signals into images as network input cannot preserve the temporal correlation between signals. In the case of small datasets and complex and variable working conditions, the accuracy of fault diagnosis is low and the generalisation ability is poor. To solve the above problems, a rolling bearing fault diagnosis method based on the Gramian angular field (GAF) and an SE-ResNeXt50 transfer learning model is proposed. Firstly, the parameters of the GAF obtained from multiple experiments are selected and the one-dimensional time-series vibration signal is encoded by combining the data enhancement method, and converted into a Gramian angular difference field (GADF) diagram and a Gramian angular sum field (GASF) diagram with local time information and uniqueness. Then, a fine-tuning transfer learning strategy is used to transfer the pre-trained model parameters to an SE-ResNeXt50 model, which improves the training speed of the model and improves the overfitting problem of the model on small target datasets. Finally, the GAF diagram is used as the input to the model and a feature recalibration strategy is used to adaptively obtain the importance of each feature channel, which further improves the feature utilisation. To verify the effectiveness and superiority of the proposed method, the rolling bearing data from Case Western Reserve University are selected for experimental verification and the generalisation performance of the proposed method is tested under varying loads and different dataset scales. The results show that when there is only a small amount of data, the proposed method can still achieve high diagnosis accuracy for different loads and has better recognition accuracy and generalisation compared to other fault diagnosis methods. [ABSTRACT FROM AUTHOR]

The paper presents the research on revealing the causes of belt conveyors downtime at open-pit mining operations. By results of researches it is established that the reasons of the above mentioned problems are technological dust formed at crushing of mineral ore, further loading and unloading, and also at transporting by conveyors. Dense dust-air mixture settling on the rotating mechanisms of conveyors, gets into the bearing units and on the surfaces of friction gears, which accelerates the process of wear of parts several times. The problem of determining the dependence of the wear of the working surface of roller bearings on the intensity of accumulation of process dust deposited on the parts and units of conveying machines has been set and solved. The analysis has shown that one of the methods of protection of parts of conveyor equipment as a whole is removal of aggressive medium. [ABSTRACT FROM AUTHOR]

Condition monitoring in machines holds significant importance for early fault detection, optimizing maintenance processes, and ensuring operational continuity. In this study, a novel intelligent detection approach for rolling bearings is introduced, utilizing the Common Spatial Pattern (CSP) method to extract distinctive features related to bearing faults. By maximizing the variance ratio of signal matrices from distinct sources, CSP sets itself apart from conventional frequency-based features. This technique captures characteristic vibration patterns unique to each measurement, enabling differentiation between faulty and healthy bearings. The effectiveness of the proposed method was assessed using Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbour (k-NN) algorithms across two diverse datasets. The results indicated an 88.5% accuracy in two-class fault detection and 93.5% in fault classification when employing ANN. Comparison with traditional time domain feature sets highlighted the superior performance of CSP features, exhibiting elevated accuracy rates in both two-class and multiclass scenarios. Thus, CSP features emerge as a promising avenue for effectively monitoring bearing conditions through vibration data. [ABSTRACT FROM AUTHOR]

In this paper, we are interested in developing a new approach that combines successive variational mode decomposition and blind source separation based on salp swarm optimization for bearing fault diagnosis. Firstly, vibration signals are pre-processed using successive variational mode decomposition to increase the signal-to-noise ratio. Then, the dynamic time-warping algorithm is adopted to select the most effective modes which will be considered mixture signals. In the second step, we apply the salp swarm algorithm (SSA) for estimating the de-mixing matrix to extract independent components from mixture signals. However, SSA suffers from the problem of population diversity. Consequently, it offers somewhat different independent sources at every execution of the program. To overcome this shortcoming, the SSA-based source estimation will be executed several times with different ranges of initial positions. Then, a fuzzy C-mean algorithm is introduced to select the reliable independent components. The suggested method is tested based on two experiments and compared to state-of-the-art methods. The obtained results demonstrate the effectiveness of the suggested method in recovering reliable independent components and extracting the fault frequency of bearings. [ABSTRACT FROM AUTHOR]

In the last decade, research centered around the fault diagnosis of rotating machinery using non-contact techniques has been significantly on the rise. For the first time worldwide, innovative techniques for the diagnosis of rotating machinery, based on electrical motors, including generic, nonlinear, higher-order cross-correlations of spectral moduli of the third and fourth order (CCSM3 and CCSM4, respectively), have been comprehensively validated by modeling and experiments. The existing higher-order cross-correlations of complex spectra are not sufficiently effective for the fault diagnosis of rotating machinery. The novel technology CCSM3 was comprehensively experimentally validated for induction motor bearing diagnosis via motor current signals. Experimental results, provided by the validated technology, confirmed high overall probabilities of correct diagnosis for bearings at early stages of damage development. The novel diagnosis technologies were compared with existing diagnosis technologies, based on triple and fourth cross-correlations of the complex spectra. The comprehensive validation and comparison of the novel cross-correlation technologies confirmed an important non-traditional novel outcome: the technologies based on cross-correlations of spectral moduli were more effective for damage diagnosis than the technologies based on cross-correlations of the complex spectra. Experimental and simulation validations confirmed a high probability of correct diagnosis via the CCSM at the early stage of fault development. The average total probability of incorrect diagnosis for the CCSM3 for all experimental results of 8 tested bearings, estimated via 6528 diagnostic features, was 1.475%. The effectiveness gains in the total probability of incorrect diagnosis for the CCSM3 in comparison with the CCCS3 were 26.8 for the experimental validation and 18.9 for the simulation validation. The effectiveness gains in the Fisher criterion for the CCSM3 in comparison with the CCCS3 were 50.7 for the simulation validation and 104.7 for the experimental validation. [ABSTRACT FROM AUTHOR]

Condition monitoring of wind turbines is progressively increasing to maintain the continuity of clean energy supply to power grids. This issue is of great importance since it prevents wind turbines from failing and overheating, as most wind turbines with doubly fed induction generators (DFIG) are overheated due to faults in generator bearings. Bearing fault detection has become a main topic targeting the optimum operation, unscheduled downtime, and maintenance cost of turbine generators. Wind turbines are equipped with condition monitoring devices. However, effective and reliable fault detection still faces significant difficulties. As the majority of health monitoring techniques are primarily focused on a single operating condition, they are unable to effectively determine the health condition of turbines, which results in unwanted downtimes. New and reliable strategies for data analysis were incorporated into this research, given the large amount and variety of data. The development of a new model of the temperature of the DFIG bearing versus wind speed to identify false alarms is the key innovation of this work. This research aims to analyze the parameters for condition monitoring of DFIG bearings using SCADA data for k-means clustering training. The variables of k are obtained by the elbow method that revealed three classes of k (k = 0, 1, and 2). Box plot visualization is used to quantify data points. The average rotation speed and average temperature measurement of the DFIG bearings are found to be primary indicators to characterize normal or irregular operating conditions. In order to evaluate the performance of the clustering model, an analysis of the assessment indices is also executed. The ultimate goal of the study is to be able to use SCADA-recorded data to provide advance warning of failures or performance issues. [ABSTRACT FROM AUTHOR]

With the increasing complexity of CNC machine tools and other rotating machinery, it becomes more and more important to improve the reliability of such machines. It is necessary to estimate the remaining useful life (RUL) of the important parts such as bearings of these devices. However, the operating conditions of such parts are often very complicated, and there is a great difference between different devices. Therefore, it is difficult to use the traditional mechanism analysis method, which not only is very hard, but also generally has low prediction accuracy. In order to solve the above problems, this paper proposes a feature-transferred prediction network (FTPN), which can adapt to various working conditions, combining with the neural network method in the field of artificial intelligence, and effectively realizes RUL prediction. Since the existing neural network methods were originally proposed to solve the problems in other fields, such as semantic recognition, this paper uses the feature transfer method to improve them. Firstly, the source network based on convolutional neural network (CNN) is pre-trained for fault recognition, and the fault feature information extracted after training is stored in the fault feature layer. Second, CNN and gate recurrent unit (GRU) are used to build target networks and fit the relationship between time series and remaining longevity. Finally, a special loss function is designed to transfer the features extracted from the source network to the target network to help the target network learn fault features and better predict the mechanical RUL. In order to verify the effectiveness of the proposed method, experiments are carried out using the public data set of accelerated life of bearings, and high prediction accuracy is obtained, which proves that the proposed method has certain generalization. The comparison with the existing methods on the same data set shows that the proposed method has a broad industrial application prospect. [ABSTRACT FROM AUTHOR]

Vibration monitoring of CNC high-speed machining (HSM) centers under non-stationary conditions, characterized by varying operating parameters and uncertainties affected by the change of speed and load during operation currently presents a particular challenge. Therefore, bearing condition monitoring is important. Indeed, this variation has a considerable impact on the vibratory response delivered by the accelerometers and therefore can mask any fault. The change in speed causes considerable changes in the spectrum of the vibration such that defect signatures become almost undetectable with conventional tools. The order tracking method based on time–frequency representation is regarded as an effective tool for fault detection of bearings with varying rotating speeds. This study aims to propose non-stationary tools based on tachometer order tracking to detect bearing faults in high-speed milling centers during run-up and coast-down conditions. Developed tools are compared to stationary technics in this study, remaining limited to detect faults. Indeed, the speed variation would cause spectrum smearing if classic tools are used in non-stationary conditions. These latter methods are based on constant rotating speed and would fail to detect faults of bearings with variable spindle rotating speeds. [ABSTRACT FROM AUTHOR]

To solve the problem of fault signals of wind turbine bearings being weak, not easy to extract, and difficult to identify, this paper proposes a fault diagnosis method for fan bearings based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Grey Wolf Algorithm Optimization Kernel Extreme Learning Machine (GWO-KELM). First, eliminating the interference of noise on the collected vibration signal should be conducted, in which the wavelet threshold denoising approach is used in order to reduce the noise interference with the vibration signal. Next, CEEMDAN is used to decompose the signal after a denoising operation to obtain the multi-group intrinsic mode function (IMF), and the feature vector is selected by combining the correlation coefficients to eliminate the spurious feature components. Finally, the fuzzy entropy for the chosen IMF component is input into the GWO-KELM model as a feature vector for defect detection. After diagnosing the Case Western Reserve University (CWRU) dataset by the method presented in this research, it is found that the method can identify 99.42% of the various bearing states. When compared to existing combination approaches, the proposed method is shown to be more efficient for diagnosing wind turbine bearing faults. [ABSTRACT FROM AUTHOR]

The rotating components of a gas turbine engine are typically designed around a perfectly straight centerline. In spite of advanced manufacturing technology and the conviction of the human eye, straightness is virtually impossible to achieve during manufacturing and assembly. High-tech metrology can quantify ever so slight centerline deviations along the unconstrained rotor assembly which are called bends. Related phenomena are rotor bow and thermal bow, the latter of which is normally due to asymmetric cooling after engine shutdown. Yet, bent and bowed rotors differ from one another in that bends are permanent deviations from the centerline of the unconstrained rotor, whereas rotor bow is temporary, typically elastic, and observed in the mounted, and, therefore, constrained rotor assembly. Greater complexity is introduced with the realization that a bent rotor can additionally be subject to rotor bow. The presence of bends leads to force and moment distributions along the rotating structure that can have significant dynamic implications for even very small bends. In opposition to unbalance loads, which increase with rotor speed, the rotating excitation of a bent rotor remains constant. The equations of motion (EOM) of a bent rotor are well defined in the literature. However, the analysis is usually confined to simplified cases where said centerline deviations at the bearing supports are zero. For realistic rotor applications, this is not the case and additional static analysis is required to obtain the proper dynamic load distribution along the rotor. In this paper, the finite element method (FEM) is used to analyze bent rotors within an MSC NASTRAN v2021 work-flow that can address rotor models of any complexity. The proposed approach can also account for static, compliant, and greatly featured support structures that communicate with the rotor model via its common, and potentially misaligned, bearing supports. Angular and lateral offsets are explored in three different scenarios of two rotor configurations: scenarios 1 and 2 introduce a simply bent rotor, along which synchronous force and moment distributions are computed (due to its intrinsic deviations) to subsequently excite the bent rotor dynamically. While scenario 1 requires an initial static analysis with enforced displacements to accomplish this task, the equivalent dynamic excitation of scenario 2 can be computed directly due to perfect bearing alignment. In scenario 3, the complexity of the rotor bend is increased to four angular kinks and four lateral offsets to suggest the deployment of this method in combination with high-tech metrology equipment that can produce a large number of such measurements via automated probing or scanning technologies. In a final step, the bent rotor is augmented with unbalances and compared to its nominal counterpart to deliver the motivation for this method and its value to the turbomachinery community. All results of scenarios 1, 2, and 3 are verified against an experimentally validated transfer matrix method (TMM). [ABSTRACT FROM AUTHOR]

Bearings are massively utilized in industries of nowadays due to their huge importance. Nevertheless, their defects can heavily affect the machines performance. Therefore, many researchers are working on bearing fault detection and classification; however, most of the works are carried out under constant speed conditions, while bearings usually operate under varying speed conditions making the task more challenging. In this paper, we propose a new method for bearing condition monitoring under time-varying speed that is able to detect the fault efficiently from the vibration signatures. First, the vibration signal is processed with the Empirical Wavelet Transform to extract the AM-FM modes. Next, time domain features are calculated from each mode. Then, the features' set is reduced using the Cultural Clan-based optimization algorithm by removing the redundant and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm "Random Forest" is used to train a model able to classify the fault based on the selected features. The proposed method was tested on a time-varying real dataset consisting of three different bearing health states: healthy, outer race defect, and inner race defect. The obtained results indicate the ability of our proposed method to handle the speed variability issue in bearing fault detection with high efficiency. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has revolutionized condition monitoring (CoM) in mechanical systems by reducing manual signal processing. However, DL's industrial integration is limited due to low robustness against distribution shifts. Existing CoM approaches focus on adaptating to one-time distribution shift. We introduce Continuous Unsupervised Domain Adaptation-based CoM (CUDACoM), tackling continuous distribution shifts in systems under perpetually dynamic conditions. CUDACoM mitigates confirmation bias, detrimental in long-domain sequences, by introducing two novel strategies (1) Fresh Initialization and (2) In-Domain Pseudo Labeling. Fresh Initialization maintains high model plasticity, while In-Domain pseudo-labeling improves pseudo-label accuracy, enhancing model adaptability. These strategies reduce confirmation bias, crucial for robust self-training, making CUDACoM ideal for long-sequence domain adaptation in perpetually dynamic environments. CUDACoM outperforms state-of-the-art (SOTA) adversarial and self-training approaches. Validated through two practical case studies: a 200% (RPM) change and gradual sensor degradation across 40 noise levels. These challenging case studies show stronger data shifts than the commonly used standard benchmarking datasets. The second case is especially novel, formulating robustness to noise as a domain adaptation problem. CUDACoM achieved a test accuracy of 0.937 in the RPM case (vs. SOTA's 0.770) and 0.849 in the sensor degradation case (vs. SOTA's 0.751). This study addresses the overlooked challenges of employing DL for CoM in perpetually dynamic environments, particularly, the confirmation bias. With CUDACoM's computational efficiency, we provide a practical solution that enhances reliability by facilitating the integration of robust DL into industrial CoM systems. [ABSTRACT FROM AUTHOR]

• The degradation of tensile and fatigue properties of high-speed train bearing after long-term service were explored. • The decline rates of fatigue strength in bearing steel after long-term service are 20.0% and 41.2%, respectively. • The mean length of secondary cracks increases from 8.3 μm to 9.8 μm, which indicating that more cracks initiated in used bearing. • It would provide a new insight for the mechanical property evolution law in high-speed train bearing. The remaining useful life prediction of the bearings after long-term service is a crucial issue. Exploring the fatigue failure mechanism of bearing is helpful to grasp the dynamic evolution law during its whole period. In this paper, the tensile and fatigue property degradation law of bearings for high-speed train under different service mileages were explored. The results display that there is a downward trend for the tensile/fatigue strength and elongation of the bearing as prolonging its service mileage. The area proportion of dimples declines while that of cleavage plane increases in bearing steel after long-term service. The variations of secondary crack length and equivalent diameters of cleavage plane indicate that more cracks initiated in damaged bearing under cyclic loading. Based on the energy principle, the fatigue failure mechanism of bearings after long-term service was elaborated with the plastic deformation, crack initiation and growth resistance. The results would provide theoretical reference for the fatigue property degradation law of high-speed train bearings. [ABSTRACT FROM AUTHOR]

Rolling bearings are a critical component of mechanical transmission equipment. Predicting their degradation trend is crucial for ensuring safe and stable equipment operation. Most existing bearing degradation prediction methods based on state space models (SSMs) use either linear functions or limited nonlinear functions (e.g., exponential/power laws) to construct the state and measurement equations. As such, these models fail to adapt to the complex and varied nonlinear degradation processes that occur in real-world environments. To address this limitation, we developed a deep latent variable-driven state space degradation model and employed it for bearing degradation prediction. Owing to the powerful nonlinear modeling ability of deep learning models, the proposed method extends the applicability of state space equations. In addition, it inherits the advantages of SSMs and can model uncertainties in a structured manner. Furthermore, the model was integrated with differential pre-transformation to improve its long-term prediction performance. Finally, to validate the effectiveness of the proposed model in predicting bearing degradation, experiments were conducted using a bearing dataset from the PRONOSTIA platform and real wind turbine bearing data. Results showed that the proposed method yielded higher bearing degradation prediction accuracy than existing methods, thus demonstrating the superior performance of the proposed model in predicting bearing degradation. [ABSTRACT FROM AUTHOR]

Introduction: Optimal bearing size in total hip arthroplasty (THA) has not been clearly ascertained, but large diameter ceramic balls may improve function and implant stability, with minimal concerns for wear. Delta ceramic-on-ceramic (COC) THA were stratified based on head diameter (32, 36 and 40 mm), aiming to assess: 1) survival rates, reasons for revision and hazard ratios for failure; 2) survival rates and hazard ratios for aseptic loosening, dislocation/primary instability, liner failure.A regional arthroplasty registry was enquired about Delta COC THA performed for primary osteoarthritis, stratified by head size. 13,161 primary cementless THAs were included in the study, with a mean follow-up of 5.7 years (range: 0–17): 3980 (30.2%) 32 mm balls, 8327 (63.3%) 36 mm heads, 854 (6.5%) 40 mm heads.The three cohorts achieved similar survival rates (p = 0.99) and adjusted hazard ratios (p > 0.05). No revisions for head breakage or metallosis occurred. When revision for cup aseptic loosening was the endpoint, the survival rates of three cohorts (p = 0.08) and the adjusted hazard ratios (p > 0.05) were similar. The three cohorts achieved comparable survival rates when revision for dislocation/primary instability was the endpoint (p = 0.08). When the endpoint was revision for liner breakage, 32 mm cohort showed higher rates of revision (p = 0.01). No liner failure was detected in the 40 mm cohort.32 mm, 36 mm and 40 mm provided similar 10-year survival rates in Delta COC THA. 40 mm heads emerged as safe options but did not lower the revisions for dislocations/primary instability.IV (therapeutic study).Materials and methods: Optimal bearing size in total hip arthroplasty (THA) has not been clearly ascertained, but large diameter ceramic balls may improve function and implant stability, with minimal concerns for wear. Delta ceramic-on-ceramic (COC) THA were stratified based on head diameter (32, 36 and 40 mm), aiming to assess: 1) survival rates, reasons for revision and hazard ratios for failure; 2) survival rates and hazard ratios for aseptic loosening, dislocation/primary instability, liner failure.A regional arthroplasty registry was enquired about Delta COC THA performed for primary osteoarthritis, stratified by head size. 13,161 primary cementless THAs were included in the study, with a mean follow-up of 5.7 years (range: 0–17): 3980 (30.2%) 32 mm balls, 8327 (63.3%) 36 mm heads, 854 (6.5%) 40 mm heads.The three cohorts achieved similar survival rates (p = 0.99) and adjusted hazard ratios (p > 0.05). No revisions for head breakage or metallosis occurred. When revision for cup aseptic loosening was the endpoint, the survival rates of three cohorts (p = 0.08) and the adjusted hazard ratios (p > 0.05) were similar. The three cohorts achieved comparable survival rates when revision for dislocation/primary instability was the endpoint (p = 0.08). When the endpoint was revision for liner breakage, 32 mm cohort showed higher rates of revision (p = 0.01). No liner failure was detected in the 40 mm cohort.32 mm, 36 mm and 40 mm provided similar 10-year survival rates in Delta COC THA. 40 mm heads emerged as safe options but did not lower the revisions for dislocations/primary instability.IV (therapeutic study).Results: Optimal bearing size in total hip arthroplasty (THA) has not been clearly ascertained, but large diameter ceramic balls may improve function and implant stability, with minimal concerns for wear. Delta ceramic-on-ceramic (COC) THA were stratified based on head diameter (32, 36 and 40 mm), aiming to assess: 1) survival rates, reasons for revision and hazard ratios for failure; 2) survival rates and hazard ratios for aseptic loosening, dislocation/primary instability, liner failure.A regional arthroplasty registry was enquired about Delta COC THA performed for primary osteoarthritis, stratified by head size. 13,161 primary cementless THAs were included in the study, with a mean follow-up of 5.7 years (range: 0–17): 3980 (30.2%) 32 mm balls, 8327 (63.3%) 36 mm heads, 854 (6.5%) 40 mm heads.The three cohorts achieved similar survival rates (p = 0.99) and adjusted hazard ratios (p > 0.05). No revisions for head breakage or metallosis occurred. When revision for cup aseptic loosening was the endpoint, the survival rates of three cohorts (p = 0.08) and the adjusted hazard ratios (p > 0.05) were similar. The three cohorts achieved comparable survival rates when revision for dislocation/primary instability was the endpoint (p = 0.08). When the endpoint was revision for liner breakage, 32 mm cohort showed higher rates of revision (p = 0.01). No liner failure was detected in the 40 mm cohort.32 mm, 36 mm and 40 mm provided similar 10-year survival rates in Delta COC THA. 40 mm heads emerged as safe options but did not lower the revisions for dislocations/primary instability.IV (therapeutic study).Conclusions: Optimal bearing size in total hip arthroplasty (THA) has not been clearly ascertained, but large diameter ceramic balls may improve function and implant stability, with minimal concerns for wear. Delta ceramic-on-ceramic (COC) THA were stratified based on head diameter (32, 36 and 40 mm), aiming to assess: 1) survival rates, reasons for revision and hazard ratios for failure; 2) survival rates and hazard ratios for aseptic loosening, dislocation/primary instability, liner failure.A regional arthroplasty registry was enquired about Delta COC THA performed for primary osteoarthritis, stratified by head size. 13,161 primary cementless THAs were included in the study, with a mean follow-up of 5.7 years (range: 0–17): 3980 (30.2%) 32 mm balls, 8327 (63.3%) 36 mm heads, 854 (6.5%) 40 mm heads.The three cohorts achieved similar survival rates (p = 0.99) and adjusted hazard ratios (p > 0.05). No revisions for head breakage or metallosis occurred. When revision for cup aseptic loosening was the endpoint, the survival rates of three cohorts (p = 0.08) and the adjusted hazard ratios (p > 0.05) were similar. The three cohorts achieved comparable survival rates when revision for dislocation/primary instability was the endpoint (p = 0.08). When the endpoint was revision for liner breakage, 32 mm cohort showed higher rates of revision (p = 0.01). No liner failure was detected in the 40 mm cohort.32 mm, 36 mm and 40 mm provided similar 10-year survival rates in Delta COC THA. 40 mm heads emerged as safe options but did not lower the revisions for dislocations/primary instability.IV (therapeutic study).Level of Evidence: Optimal bearing size in total hip arthroplasty (THA) has not been clearly ascertained, but large diameter ceramic balls may improve function and implant stability, with minimal concerns for wear. Delta ceramic-on-ceramic (COC) THA were stratified based on head diameter (32, 36 and 40 mm), aiming to assess: 1) survival rates, reasons for revision and hazard ratios for failure; 2) survival rates and hazard ratios for aseptic loosening, dislocation/primary instability, liner failure.A regional arthroplasty registry was enquired about Delta COC THA performed for primary osteoarthritis, stratified by head size. 13,161 primary cementless THAs were included in the study, with a mean follow-up of 5.7 years (range: 0–17): 3980 (30.2%) 32 mm balls, 8327 (63.3%) 36 mm heads, 854 (6.5%) 40 mm heads.The three cohorts achieved similar survival rates (p = 0.99) and adjusted hazard ratios (p > 0.05). No revisions for head breakage or metallosis occurred. When revision for cup aseptic loosening was the endpoint, the survival rates of three cohorts (p = 0.08) and the adjusted hazard ratios (p > 0.05) were similar. The three cohorts achieved comparable survival rates when revision for dislocation/primary instability was the endpoint (p = 0.08). When the endpoint was revision for liner breakage, 32 mm cohort showed higher rates of revision (p = 0.01). No liner failure was detected in the 40 mm cohort.32 mm, 36 mm and 40 mm provided similar 10-year survival rates in Delta COC THA. 40 mm heads emerged as safe options but did not lower the revisions for dislocations/primary instability.IV (therapeutic study). [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to address the influence of lubrication methods on operational characteristics, power losses and temperature behavior of gears and bearings. It contributes to the improvement of resource and energy efficiency of geared transmissions. Design/methodology/approach: Experimental investigations were performed at a gear and bearing power loss test rig. Thereby, dip lubrication, injection lubrication with injection volumes from 0.05 to 2.00 l/min and minimum quantity (MQ) lubrication with an injection volume as little as 28 ml/h were considered. Measurements were evaluated in terms of no-load and load-dependent power loss, bulk temperatures and mean gear coefficients of friction. Findings: Results show strongly reduced no-load gear and bearing losses for lubrication methods with low lubricant quantities. Load-dependent losses are similar to conventional lubrication methods and tend to be lower at high speed. This is related to higher bulk temperatures, as the heat dissipation of lubrication methods with low oil quantities is limited. Limited thermal load limits were shown to be extended by LowLoss gears. Originality/value: Systematic investigations were conducted to evaluate the influence of dip, injection and MQ lubrication on power loss and temperature behavior of gears and bearings. The results of this study support further research on needs-based lubrication methods for gearboxes. [ABSTRACT FROM AUTHOR]

The article provides a review of the designs of rolling bearings (given in modern literature, patents and author's certificates), including designs that improve the load capacity and durability of bearings, reduce their wear and provide bearing lubrication. [ABSTRACT FROM AUTHOR]

For mobile robots, localization is essential for navigation and spatial correlation of its collected data. However, localization in Global Positioning System-denied environments such as underwater has been challenging. Light-emitting diode (LED)-based optical localization has been proposed in the literature, where the bearing angles extracted from the line-of-sight of the robot viewed from a pair of base nodes (also known as beacon nodes) are used to triangulate the position of the robot. The state-of-the-art in this approach uses a stop-and-go motion for the robot in order to ensure an accurate position measurement, which severely limits the mobility of the robot. This work presents an LED-based optical localization scheme for a mobile robot undergoing continuous motion, despite the two angles in each measurement cycle being captured at different locations of the robot. In particular, the bearing angle measurements are captured by the robot one at a time and are properly correlated with respect to the base nodes by utilizing the velocity prediction from Kalman filtering. The proposed system is evaluated in simulation and experiments, with its performance compared to the traditional state-of-the-art approach where the two angle measurements in each cycle are used directly to compute the position of the robot. In particular, the experimental results show that the average position and velocity estimation errors are reduced by 55% and 38%, respectively, when comparing the proposed method to the state-of-the-art. [ABSTRACT FROM AUTHOR]

The failure of bearings can have a significant negative impact on the safe operation of equipment. Recently, deep learning has become one of the focuses of RUL prediction due to its potent scalability and nonlinear fitting ability. The supervised learning process in deep learning requires a significant quantity of labeled data, but data labeling can be expensive and time-consuming. Cotraining is a semisupervised learning method that reduces the quantity of required labeled data through exploiting available unlabeled data in supervised learning to boost accuracy. This paper innovatively proposes a cotraining-based approach for RUL prediction. A CNN and an LSTM were cotrained on large amounts of unlabeled data to obtain a health indicator (HI), then the monitoring data were entered into the HI and the RUL prediction was realized. The effectiveness of the proposed approach was compared and analyzed against individual CNN and LSTM and the stacking networks SAE+LSTM and CNN+LSTM in the existing literature using RMSE and MAPE values on a PHM 2012 dataset. The results demonstrate that the RMSE and MAPE value of the proposed approach are superior to individual CNN and LSTM, and the RMSE value of the proposed approach is 54.72, which is significantly lower than SAE+LSTM (137.12), and close to CNN+LSTM (49.36). The proposed approach has also been tested successfully on a real-world task and thus has strong application value. [ABSTRACT FROM AUTHOR]

Mode shapes are sensitive to the structural condition of bridges but a reasonable estimate of such changes require several accelerometers, which can be resource intensive. This paper obviates this problem through a novel structure health monitoring (SHM) approach for estimating modal parameters of bridges, including damage-induced changes of boundary conditions by using progressively re-deploying sensors along a monitored bridge. This concept of re-deployable sensors and subsequent use of a series of measurements allow extracting data from different bridge segments and also to get an indication of the condition of the bridge through frequency domain decomposition. Data from different segments are combined to estimate the global mode shape of the bridge and its gradient is observed to be indicative of support stiffness change. The concept is successfully tested through a full-scale field trial on a railway bridge in the Republic of Ireland, before and after the rehabilitation of its supports. The results are expected to guide future on-site measurement of damages due to flooding, scour, and other natural hazards, along with the effectiveness of intervention actions like repair and rehabilitation, providing a clear evidence base for practical value of SHM. [ABSTRACT FROM AUTHOR]

Multi-span simply supported (MSSS) highway bridges are one of the most common bridge types across the world and have several vulnerable components, including bearings, piers, and abutments. Although several past studies have evaluated the seismic response and fragility of common multi-span simply supported bridges, there is negligible research that explores the influence of bearing types, and non-ductile failure modes of bridge piers on the performance of older non-seismically designed simply supported bridges. Additionally, a better understanding of seismic risk to highway transportation systems necessitates the exploration of design code advances and subsequent impact on the seismic vulnerability of MSSS highway bridges. Consequently, this study also focuses on developing and comparing fragility curves for the older design era non-seismically designed, and modern era seismically designed MSSS bridges. High-fidelity three-dimensional (3-D) analytical models, which account for the nonlinear behavior of different components, including non-ductile failure modes of bridge piers, are developed. Deterministic seismic response assessment reveals a higher seismic demand on bridge pier for MSSS bridges with steel bearings as compared to those with elastomeric bearings. The bridge component and system fragility curves are developed with a suite of ground motions representative of the seismic hazard of the case-study region. Results reveal the significant vulnerability of older non-seismically designed MSSS bridge with steel bearings due to the non-ductile failure of the bridge pier. The vulnerability of the bridge is significantly reduced with the adoption of modern seismic design and detailing practices. [ABSTRACT FROM AUTHOR]

Helicopters Swash Plates Assembly allows helicopter steering movement and is defined as a safety system. This system experiences wear and high loads during flight. Following a failure of an assembly that caused a helicopter forced landing, a novel method was developed to monitor the system for early warning based on integration of the Ferrography and Atomic Emission Spectrometry methods. The critical values of metal concentration and particle size in a sample were determined for Ferrography and Atomic Emission Spectrometry methods, respectively. The valuable results allowed developing a decision-making flowchart. The integration of the methods yielded complementary detectability and extended detection range. Over a period of 7.5 years, 37 different assemblies were monitored, yielding a wide data base of 1618 samples. Recently, four assemblies were removed from helicopters following the method alerts and found as True Positive cases, in addition one control assembly was dismantled and found True Negative. Nowadays, this method is implemented on helicopters in service. [ABSTRACT FROM AUTHOR]

• Crack behaviors under oscillation contact have significant differences from those under traditional rolling contact. •.Propagation of the crack with a small initial angle results in spitting • Increasing the friction coefficient enlarges the gaps in growth rate and angle between s01 and s02 for all initial angles. • Reducing the friction coefficient is beneficial to retard crack propagation. • The initial angle plays a significant role in crack evolution. Cracks with greater initial angles grow faster. In wind turbine bearings subjected to oscillating motion, cracks undergoing reciprocating rolling contact loading experience more complex stress changes compared to those under constant rotational motion. However, oscillation is less explored. This study introduces an Oscillation Unified Surface-Initiated Crack Finite Element Model (OU-CFEM) to address this gap. The model is validated through mesh sensitivity analysis and Oscillation Contact Fatigue (OCF) experimental data. The OU-CFEM enables a detailed investigation into crack evolution characteristics under varying initial angles and friction coefficients. The findings reveal that crack behaviors significantly differ across different oscillation phases, except in cases involving vertically oriented cracks or fully lubricated conditions. The study highlights that larger initial angles and higher friction coefficients promote crack extension, with the initial angle playing a more critical role in crack evolution than the friction coefficient. This research provides new insights into the behavior of cracks in oscillating conditions, offering valuable contributions to the understanding of wind turbine bearing durability under such loading scenarios. [ABSTRACT FROM AUTHOR]

• A novel bearing health sensing technique using Smartphone is proposed. • The technique is non-invasive, easy-to-use, robust-to-noise, and low-cost. • A real signal from noisy mining field verify the efficacy of presented technique. Bearing health sensing has drawn increasing attention as bearing plays a vulnerable yet essential role in modern industry. Nevertheless, conventional health sensing technologies involve dedicated sensor installation and high costs, which may not meet the monitoring and sensing demands. Given this, we present a health sensing technique to capture the fault information of bearing by leveraging an off-the-shelf smartphone. Specifically, we first exploit only a readily available smartphone to capture the audio signal of bearing. Given the captured audio signal, an adaptive time–frequency masking is proposed to separate the noise, and the fault impulse is then sharpened by the singular value decomposition (SVD). The extensive experimental results illustrate that our technique is able to enhance the bearing health sensing capability of smartphones under various real-world scenarios. Even in noisy mining field conditions, the framework still achieves a notable performance improvement rate of 169 %. [ABSTRACT FROM AUTHOR]

Bearings are the significant component in machinery applications to run a system; hence, the damage of bearings can stop a running machine. The bearing faults often arise because of poor tribological behavior and wrong lubrication. Several fault prediction models already exist, but if the data were too large, the conventional techniques took a wide time range, gaining very low accuracy. The present research work has focused on designing an efficient bearing faults detection system and optimized tribological characters to address this issue. Moreover, the proposed mechanism is named as Hybrid ant lion and African buffalo-based Modular Neural Frame. The dataset taken for estimating the fault is Case Western Reserve University bearing center. In addition, to gain the finest result, mineral oil is added as a lubricant to reduce friction and to enhance the bearing life. Here, the fitness of Ant lion and African buffalo was upgraded in the classification layer of the modular neural frame that helps to optimize the parameters and has improved the fault prediction ratio. In addition, the proposed model is implemented in the MATLAB R2018b environment. Finally, the parameters were calculated and compared with other models in terms of accuracy, wear, friction, precision, recall, time, F-score and have gained the finest results in all scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a novel decentralised navigation system based on bearing measurements for tiered vehicle formations. In the proposed framework, some vehicles have access to measurements of their own position, whereas others have access to bearing measurements to one or more neighbouring vehicles. Depth measurement may also be available. Local observers for the position and fluid velocity are designed based on the derivation of an equivalent observable linear time-varying system, thus yielding globally exponentially stable error dynamics. The local observers rely on local measurements, as well as limited communications between the vehicles. The stability of the system as a whole is analysed by studying the robustness of the local observers to exponentially decaying perturbations. Thorough Monte Carlo simulations are presented and discussed to compare the performance of the proposed solution with the extended Kalman filter, the unscented Kalman filter, and the Bayesian Cramér-Rao bound. [ABSTRACT FROM AUTHOR]

The rotor operating stiffness of high-speed motorized spindles (HSMSs) is key to machining accuracy. Because HSMSs are difficult to load due to their high speeds, a contact loading device was developed to test rotor operating stiffness. The dynamic support stiffness of the front/rear bearings (DSSB) is the main factor affecting the rotor operating stiffness. Two novel experimental schemes for measuring the DSSB are proposed: (1) indirect measurement—by analysing deformation displacements at two points on the external loading rod of the HSMS and (2) direct measurement—by eddy current sensors installed near the front/rear bearings. Based on the experimental device and two experimental schemes, the influences of working condition parameters on the DSSB were tested. The results show that the proposed experimental device and two experimental schemes can effectively and accurately measure rotor operating stiffness and DSSB at speeds of up to 30,000 rpm. However, because the tapered connection gap between the loading rod and rotor increases the measured deformation displacement, the DSSB measured by the indirect measurement scheme was relatively small. The DSSB decreases with speed and increases with radial force and working temperature. This study provides a new experimental basis for the quality inspection of finished HSMSs and the verification of theoretical bearing stiffness models. [ABSTRACT FROM AUTHOR]

In this paper, an efficient numerical method consisting of the real mode component mode synthesis (CMS) model reduction, shooting method with parallel computing, and Floquet analysis was developed for nonlinear rotordynamics analysis of a flexible rotor supported by a four-lobe flexure pivot tilting pad journal bearing (FPTPJB) in load-on-pad (LOP) and load-between-pad (LBP) orientations in comparison to a fixed profile journal bearing (JB) of the same pad geometry. The method used the rotor's finite elements and bearing forces obtained from directly solving the Reynolds equation to determine the limit cycles and Hopf bifurcation types. For the investigated rotor and bearing parameters, the numerical results indicated that the onset speed of instability (OSI) of FPTPJB is considerably higher than that of JB of the same orientation. Also, FPTPJB in LOP orientation yielded substantially higher OSI than the LBP one, whereas the OSI of JB in LOBP orientation was higher than the LOP counterpart. Nonlinear calculation results indicated that all bearing types and orientations gave subcritical Hopf bifurcation. The FPTPJB in LOP orientation produced the largest stable operating region, whereas the JB in LBP configuration yielded the smallest one. The experiment showed subcritical Hopf bifurcation occurred at speed close to the calculated OSI in all cases except FPTPJB in LOP orientation that the OSI is higher than the maximum test rig speed. The whirling orbit had the same frequency as the first critical speed and was precessed in the shaft rotation direction. [ABSTRACT FROM AUTHOR]

NASA is leading the design and development of a next-generation CO2 removal system, the four bed carbon dioxide scrubber (4BCO2), and intends to use the International Space Station (ISS) as its testbed. A key component of the system is the blower that provides the airflow through the CO2 sorbent beds. To improve performance and reliability, magnetic levitation (magnetic bearings) will be used in lieu of more conventional bearings (e.g., ball bearings or air bearings) to improve resistance to contaminants and enable extensibility with regards to blower speed, pressure rise and mass flow rate. The blower will pull air from the ISS through an adsorbing desiccant bed and push it through a CO2 sorbent bed and desorbing desiccant bed. The 4BCO2 blower features an overhung permanent magnet motor, a centrally located five-axis, active magnetic bearing system, backup bearings, and an overhung centrifugal impeller in a very compact package. Magnetic bearings are a natural choice for this application due to low power consumption, low transmitted vibration and oil free operation. This article describes the design considerations and design selections for the blower system with a focus on the magnetic bearings. Magnetic FEA of the actuator/sensor system, rotordynamics/controls analysis, and backup bearing drop simulations are discussed in detail. It is expected that the successful implementation of magnetic bearings for this space application will encourage the more widespread adoption in other space applications (e.g., fluid pumps, reaction wheels) that challenge conventional bearing technologies. [ABSTRACT FROM AUTHOR]

Fluid leakage from hydraulic cylinders is a major concern for the offshore industries as it directly affects hydraulic cylinder energy efficiency and causes environmental contamination. There have been attempts made in literature to develop robust condition monitoring techniques for hydraulic cylinders. However, most of these studies were performed to identify degradation of single components. Therefore, in this study, the aim is to monitor degradation of multiple components simultaneously in hydraulic cylinders using acoustic emissions. Experiments performed consist of three test phases and were performed using a hydraulic test rig. In the first test phase, the study is performed to identify acoustic emission features that can be used to monitor piston rod seal wear. In the second test phase, acoustic emission features are identified that can be used to understand bearing wear when unworn, semi-worn or worn piston rod seals are used in hydraulic test rig. In the third test phase, a run-to-failure test is conducted to identify acoustic emission features that can indicate fluid leakage initiation due to piston rod seal wear. The median frequency feature showed good repeatability in all the three test phases to identify piston rod seal wear, bearing wear and fluid leakage initiation during the initial stages in the hydraulic test rig. The proposed acoustic emission-based condition monitoring technique is robust and can be used for the hydraulic cylinders in the industries, as it identifies acoustic emission features based on particular frequency bands associated to specific components, making it less susceptible to noise from other components. [ABSTRACT FROM AUTHOR]

Advanced SSAs (e.g., the Mark III (MKIII)) were designed to increase mobility by eliminating the volume change associated with bending joints by using constant-volume rigid components with bearings connecting these components. Even with these changes, there are added torques required by the operator to drive the motion, which increases the energy expenditure with respect to unsuited motion. Part of the added effort stems from the mass and inertia of the suit, as well as frictional resistances to motion. This research considers the relationship between joint torques that an operator must generate and the resulting flexion/extension of the hip bearing assembly. A computational dynamics model of the MKIII inclusive of inertial and bearing friction properties was created and sensitivities of the model to input parameters (e.g., applied force, direction of gravity, bearing friction magnitude, knee angle) were investigated. The model was configured to match previously collected benchtop experimental suit data without a human that was externally forced. The model captured the hysteretic behaviour and estimated about 80% of the mean hip angle range as compared to the experimental data. Decreasing bearing resistance increased alignment with the experimental data. The torque due to inertia and friction each had periods where they dominated the total torque, supporting the importance of minimizing both mass and bearing friction. The present effort also highlighted how external forces and boundary conditions affected peak hip flexion/extension. Future efforts can use these types of dynamics models to examine motions driven internally by a person to achieve specific motions. [ABSTRACT FROM AUTHOR]

The failure of bearings is a prevalent cause of machinery breakdowns. The rapid development of intelligent technology has significantly promoted the use of deep learning techniques for identifying problems with machinery bearings. To achieve accuracy, deep learning-based diagnostic techniques require a substantial and uniformly diversified amount of training data. However, obtaining artificial labels for bearing fault data poses a major obstacle in engineering practice. This paper proposes an intelligent fault diagnosis method for bearings based on an improved convolutional neural network (CNN) to address the challenges of small training data and imbalanced distribution. To enable intelligent diagnosis of bearings with a small sample and imbalanced distribution, a clustering loss layer is introduced into the CNN. Furthermore, we optimize the parameters of the CNN by utilizing back-propagation of both the clustering loss function and the cross-entropy loss function. This optimization process improves the accuracy of fault diagnosis. Finally, the proposed method is applied to diagnose bearing faults and analyze the simulation results. The simulation results demonstrate the effectiveness of the method in handling small data volumes and imbalanced data distributions, as well as its strong generalization performance. [ABSTRACT FROM AUTHOR]

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning maintenance engineers of impending failure or identifying current system health conditions. However, single operators may not have sufficiently large fleets of systems or component units to collect sufficient data to develop data-driven algorithms. One potential solution to overcome the challenge of having limited representative datasets is to merge datasets from multiple operators with the same type of assets. However, directly sharing data across the company's borders yields privacy concerns. Federated learning (FL) has emerged as a promising solution to leverage datasets from multiple operators to train a decentralized asset fault diagnosis model while maintaining data confidentiality. However, the performance of traditional FL algorithms degrades when local clients' datasets are heterogeneous. The dataset heterogeneity is particularly prevalent in fault diagnosis applications due to the high diversity of operating conditions and system configurations. To address this challenge, this paper proposes a novel clustering-based FL algorithm where clients are clustered based on their dataset similarity. Estimating dataset similarity between clients without explicitly sharing data is achieved by training probabilistic deep learning models and having each client examine the predictive uncertainty of the other clients' models on its local dataset. Clients are then clustered for FL based on relative prediction accuracy and uncertainty. Experiments on three bearing fault datasets, two publicly available and one newly collected for this work, show that our algorithm significantly outperforms FedAvg and a cosine similarity-based algorithm by 5.1% and 30.7% on average over the three datasets. Further, using a probabilistic classification model has the additional advantage of accurately quantifying its predictive uncertainty, which we show it does exceptionally well. [ABSTRACT FROM AUTHOR]

The aim of the study was to analyse the anti-wear diamond-like carbon (DLC) coatings produced by Plasma-Enhanced Chemical Vapour Deposition. The DLC coatings were deposited on the rings of rolling bearings operating in belt conveyors. The service life of the bearings was 1,5 years. The bearings were exposed to harsh environmental conditions. After the disassembly of the bearings, the influence of the coatings on the improvement of tribosystem characteristics was assessed. Characterization of the bearing geometry was carried out using an optical surface profilometer. The use of diamond-like coatings on rolling bearings extended the tribosystem service life in industrial conditions threefold. [ABSTRACT FROM AUTHOR]

Gas foil bearings (GFBs) are suitable for high speed and temperature applications where conventional lubricated bearing solution are not feasible. This requires the prediction of bearing temperatures and thus a thermal model considering the heat generation and heat flow paths in the bearing. The effects of two different bump foil stiffness (Iordanoff, I., 1999, "Analysis of an Aerodynamic Compliant Foil Thrust Bearing: Method for a Rapid Design," ASME J. Tribol., 121(4), pp. 816-822; Le Lez, S., Arghir, M., and Frene, J., 2007, "A New Bump-Type Foil Bearing Structure Analytical Model," ASME J. Eng. Gas Turbines Power, 129(4), pp. 1047-1057.) and heat transfer models (a simplified and a detailed one) are presented in respect to measured temperatures from literature (Radil, K., and Zeszotek, M., 2004, "An Experimental Investigation Into the Temperature Profile of a Compliant Foil Air Bearing," Tribol. Trans., 47(4), pp. 470-479; Sim, K., and Kim, T. H., 2012, "Thermohydrodynamic Analysis of Bump-Type Gas Foil Bearings Using Bump Thermal Contact and Inlet Flow Mixing Models," Tribol. Int., 48, pp. 137-148). The comparison is drawn over a wide range of operational conditions as well as measuring positions, which in such detail has not been shown before. While good agreement is found for some of the conditions and positions, only reasonable agreement is found for others. The deviations and difficulties in validating a thermal model against experiments are highlighted in a discussion about various temperature influencing parameters, especially concerning the change of clearance during operation. In conclusion, it is found that the models are able to predict temperatures reasonably well, but require delicate fine-tuning to achieve these results. Finally, the impact of temperature distribution on the maximum load capacity is evaluated by comparing predictions between an isothermal model and one including thermal effects. [ABSTRACT FROM AUTHOR]