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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]

The increasing complexity of modern mechanical systems, especially rotating machinery, demands effective condition monitoring techniques, particularly deep learning, to predict potential failures in a timely manner and enable preventative maintenance strategies. Health monitoring data analysis, a widely used approach, faces challenges due to data randomness and interpretation difficulties, highlighting the importance of robust data quality analysis for reliable monitoring. This paper presents a two-part approach to address these challenges. The first part focuses on comprehensive data preprocessing using only feature scaling and selection via random forest (RF) algorithm, streamlining the process by minimizing human intervention while managing data complexity. The second part introduces a Recurrent Expansion Network (RexNet) composed of multiple layers built on recursive expansion theories from multi-model deep learning. Unlike traditional Rex architectures, this unified framework allows fine tuning of RexNet hyperparameters, simplifying their application. By combining data quality analysis with RexNet, this methodology explores multi-model behaviors and deeper interactions between dependent (e.g., health and condition indicators) and independent variables (e.g., Remaining Useful Life (RUL)), offering richer insights than conventional methods. Both RF and RexNet undergo hyperparameter optimization using Bayesian methods under variability reduction (i.e., standard deviation) of residuals, allowing the algorithms to reach optimal solutions and enabling fair comparisons with state-of-the-art approaches. Applied to high-speed bearings using a large wind turbine dataset, this approach achieves a coefficient of determination of 0.9504, enhancing RUL prediction. This allows for more precise maintenance scheduling from imperfect predictions, reducing downtime and operational costs while improving system reliability under varying conditions. [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]

Copyright of Journal of Drainage & Irrigation Machinery Engineering / Paiguan Jixie Gongcheng Xuebao is the property of Editorial Department of Drainage & Irrigation Machinery Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

With the optimization and advancement of industrial production and manufacturing, the application scenarios of bearings have become increasingly diverse and highly coupled. This complexity poses significant challenges for the extraction of bearing fault features, consequently affecting the accuracy of cross-condition fault diagnosis methods. To improve the extraction and recognition of fault features and enhance the diagnostic accuracy of models across different conditions, this paper proposes a cross-condition bearing diagnosis method. This method, named MCR-KAResNet-TLDAF, is based on image fusion and a residual network that incorporates the Kolmogorov–Arnold representation theorem. Firstly, the one-dimensional vibration signals of the bearing are processed using Markov transition field (MTF), continuous wavelet transform (CWT), and recurrence plot (RP) methods, converting the resulting images to grayscale. These grayscale images are then multiplied by corresponding coefficients and fed into the R, G, and B channels for image fusion. Subsequently, fault features are extracted using a residual network enhanced by the Kolmogorov–Arnold representation theorem. Additionally, a domain adaptation algorithm combining multiple kernel maximum mean discrepancy (MK-MMD) and conditional domain adversarial network with entropy conditioning (CDAN+E) is employed to align the source and target domains, thereby enhancing the model's cross-condition diagnostic accuracy. The proposed method was experimentally validated on the Case Western Reserve University (CWRU) dataset and the Jiangnan University (JUN) dataset, which include the 6205-2RS JEM SKF, N205, and NU205 bearing models. The method achieved accuracy rates of 99.36% and 99.889% on the two datasets, respectively. Comparative experiments from various perspectives further confirm the superiority and effectiveness of the proposed model. [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]

Bearing life calculation is a well-researched and standardized topic for rotating operation conditions. However, there is still no validated and standardized calculation for oscillating operation, only different calculation approaches. Due to the increasing number of oscillating rolling bearings, for example, in wind turbines, industrial robots, or 3D printers, it is becoming more and more important to validate one of these approaches or to formulate a new one. In order to achieve this goal, the damage mechanisms for oscillating operating conditions must first be analyzed in more detail by means of experimental investigations. The open question is whether fatigue is the relevant damage mechanism or whether wear damage, such as fretting corrosion or false brinelling, dominates. The present work therefore shows under which oscillation angle and frequency fatigue occur in oil-lubricated cylindrical roller bearings. [ABSTRACT FROM AUTHOR]

Durability and reliability have been studied for decades through intensive trial-error experimentation. However, there are numerous fields of application where the costs associated with this approach are not acceptable. In lubricated machines with severe dynamic loads, such as high-power-density engines, simulation tools offer clear advantages over intensive testing. Prototypes and multiple scenarios can be cost-effectively simulated to assess different lubricants and engine configurations. The work presented here details the study of wear based on a validated elastohydrodynamic (EHD) simulation model of the connecting rod journal bearing. This model accounts for elastic deformation through a connecting rod finite element model (FEM). In addition, multiple lubricant rheological and tribological dependences, determined by specific experimental tests, are applied in the model through their interaction with the simulation software. Correspondingly, a novel wear algorithm is proposed to predict wear depth over time evolution along a proposed wear cycle based on the typical working ranges of high-performance engines. A final assessment is presented to compare 4 different ultralow-viscosity lubricants in their protective performance under severe conditions. The results show the evolution of the wear load and wear depth over the wear cycle. This evaluation is key to describing a lubricant selection procedure for high-power-density engines. [ABSTRACT FROM AUTHOR]

Bearings are one of the most essential elements in an induction motor, and they are built with different materials and constructions according to their application. These components are usually one of the most failure-prone parts of an electric motor, so correct and accurate measurements, instrumentation, and processing methods are required to prevent and detect the presence of different failures. This work develops a methodology based on the fusion of current and magnetic stray flux signals, calculation of statistical and non-statistical indicators, genetic algorithms (GAs), linear discriminant analysis (LDA), and neural networks. The proposed approach achieves a diagnostic effectiveness of 99.8% for detecting various damages in the outer race at 50 Hz frequency and 96.6% at 60 Hz. It also demonstrates 99.8% effectiveness for detecting damages in the presence of contaminants in lubrication at 50 Hz and 97% at 60 Hz. These results apply across metallic, ceramic, and hybrid bearings. [ABSTRACT FROM AUTHOR]

Wheel bearings play a crucial role in ensuring the smooth and reliable rotation of wheels in automotive applications. Grease is commonly used as a lubricant in wheel bearings. The physical properties of grease, such as viscosity, base oil type, and thickener composition, significantly influence the overall performance and longevity of wheel bearings. This study investigates the impact of grease's physical properties on wheel bearing friction torque and service life. To evaluate the effects of different grease formulations, a series of experiments were conducted using a four-ball tester, rheometer, and a custom-built test rig to measure bearing frictional torque and service life. Multiple greases with varying viscosity grades, base oil types (mineral oil, synthetic oil), and thickener compositions (lithium, diurea, and polyurea) were tested under controlled operating conditions. Friction torque measurements were obtained using a high-accuracy torque sensor, while service life was determined by monitoring the bearing life in actual endurance testing. The results indicated that grease physical properties directly affected wheel bearing friction torque and service life. Low to Medium-viscosity greases generally exhibited 35–40% lower friction torque due to enhanced lubrication film formation and reduced metal-to-metal contact however, excessively high viscosity resulted in 50–60% higher frictional torque and thereby increased energy consumption. Different base oil types showed varying friction reduction and service life level, with mineral oil in some cases showed lower frictional torque around 30–40% particularly at high temperature but with significant reduction in the service life. Furthermore, the thickener composition significantly influenced grease performance. Based on these findings, selecting the appropriate grease with optimal physical properties is crucial for minimizing friction torque and extending the service life of wheel bearings. [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]

In the defense sector, where mission success often hinges on the reliability of complex mechanical systems, the health of bearings within aircraft, naval vessels, ground vehicles, missile systems, drones, and robotic platforms is paramount. Different signal processing techniques along with Higher Order Spectral Analysis (HOSA) have been used in literature for the fault diagnosis of bearings. Bispectral analysis offers a valuable means of finding higher-order statistical associations within signals, thus proving to detect the nonlinearities among Gaussian and non-Gaussian data. Their resilience to noise and capacity to unveil concealed information render them advantageous across a range of applications. Therefore, this research proposesa novel approach of utilizing the features extracted directly from the Bispectrum for classifying the bearing faults, departing from the common practice in other literature where the Bispectrum is treated as an image for fault classification. In this work vibration signalsare used to detect the bearing faults. The features from the non-redundant region and diagonal slice of the Bispectrum are used to capture the statistical and higher-order spectral characteristics of the vibration signal. A set of sixteen machine learning models, viz., Decision Trees, K-Nearest Neighbors, Naive Bayes, and Support Vector Machine, is employed to classify the bearing faults. The evaluation process involves a robust 10-fold cross-validation technique. The results reveal that the Decision Tree algorithm outperformed all others, achieving a remarkable accuracy rate of 100 %. The naive Bayes algorithm also demonstrated the least performance, with an accuracy score of 99.68 %. The results obtained from these algorithms have been compared with those achieved using Convolutional Neural Network (CNN), revealing that the training time of these algorithms is significantly shorter in comparison to CNN. [ABSTRACT FROM AUTHOR]

Spectral coherence (SCoh) consists of spectral and cyclic frequencies and exhibits unique merits in simultaneously revealing the resonance frequency band and the fault characteristic frequency (FCF) of bearing signals. Most SCoh-based methods only consider the spectral frequency information, while the cyclic frequency information is ignored. However, the fault information and interference components are difficult to distinguish when only the spectral frequency is considered. To address this challenge, a novel bidirectional weighted enhanced envelope spectrum (BWEES) analysis method is proposed in this paper. First, an improved spectral weighting method is developed, which is conducted in the spectral frequency direction to enhance the resonance frequency band that carries the fault information. An autocorrelation function is exploited to reveal the cyclic information hidden in noises and appropriate weights are assigned to the spectral frequencies according to the magnitudes of autocorrelation values. Second, a cyclic weighting function is designed, which is operated in the cyclic frequency direction to enhance the FCF and suppress noise interference. The cyclic frequency components with the highest magnitudes are selected as a basis to reconstruct the one-dimensional cyclic frequency map for assigning different weights. Finally, the two-dimensional weighted bivariable map is constructed and then converted into spectral coherence to reveal the fault features. The BWEES is tested by simulation signals and experimental data, and compared with four state-of-art methods. In particular, the kurtosis values of BWEES in four different cases are 7.637, 12.831, 15.269, and 80.269, which are higher than other methods. The Gini index values of BWEES in four different cases are 0.866, 0.812, 0.424, and 0.306, which are also the largest. The above numerical results show that BWEES can achieve better performance. [ABSTRACT FROM AUTHOR]

The elasticity of bearings as well as their clearance have an essential influence on the total arrow and, therefore, on their own pulsation. In most of the literature, this elasticity is neglected in the calculation of shaft deflections. In some work, the elasticity of the bearings has been taken into account when calculating the deflection of the mixing device shaft, but this has been carried out on the basis of a high degree of customization: the behavior of the bearings has been considered linearly elastic, which does not correspond to reality because according to the elastic response of the bearing, it is a nonlinear function of the radial displacement. When the shaft of a mixing device operates in a pressure vessel, at the outlet of the pressure vessel, the shaft is provided with a sealing device, which can be considered a third bearing. This aspect is also not taken into account in the calculation of the shaft's deflection, which leads to a certain degree of error in its determination. This study aims to highlight the influence of the elasticity of the bearings and the sealing device on the stiffness of the shaft and to propose a method that supports a calculation program for calculating the elastic line of a vertical cantilever shaft, considering the role played by the bearings in the case that they behave nonlinearly and the sealing device as the third bearing. This problem was solved both by applying our own method and with the help of the FEM. [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]

Maintenance planning is crucial for efficient operation of wind turbines, particularly in harsh conditions where degradation of critical components, such as bearings, can lead to costly downtimes and safety threats. In this context, prognostics of degradation play a vital role, enabling timely interventions to prevent failures and optimize maintenance schedules. Learning systems-based vibration analysis of bearings stands out as one of the primary methods for assessing wind turbine health. However, data complexity and challenging conditions pose significant challenges to accurate degradation assessment. This paper proposes a novel approach, Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion (UBO-EREX), which combines Extreme Learning Machines (ELM), a lightweight neural network, with Recurrent Expansion algorithms, a recently advanced representation learning technique. The UBO-EREX algorithm leverages Bayesian optimization to optimize its parameters, targeting uncertainty as an objective function to be minimized. We conducted a comprehensive study comparing UBO-EREX with basic ELM and a set of time-series adaptive deep learners, all optimized using Bayesian optimization with prediction errors as the main objective. Our results demonstrate the superior performance of UBO-EREX in terms of approximation and generalization. Specifically, UBO-EREX shows improvements of approximately 5.1460 ± 2.1338% in the coefficient of determination of generalization over deep learners and 5.7056% over ELM, respectively. Moreover, the objective search time is significantly reduced with UBO-EREX with 99.7884 ± 0.2404% over deep learners, highlighting its effectiveness in real-time degradation assessment of wind turbine bearings. Overall, our findings underscore the significance of incorporating uncertainty-aware UBO-EREX in predictive maintenance strategies for wind turbines, offering enhanced accuracy, efficiency, and robustness in degradation assessment. [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]

As a core component of automobile transmission, rolling bearings play a main role in the safety and reliability of vehicles. Existing diagnostic models often treat all features equally after feature extraction, without effectively distinguishing the importance of fault features, resulting in low accuracy and poor robustness in bearing fault diagnosis. To address this issue, a fault diagnosis model for bearings under multiple operating conditions based on feature parameterization weighting is proposed. The model utilizes a feature parameterization weighting module to categorize faults into two classes based on differences in means and implements different feature processing methods. The experimental results validate that the proposed feature parameterization weighting module effectively improves the diagnostic accuracy of the model by 8.95%. In terms of noise resistance, on two multi-condition datasets, the proposed diagnostic model achieves diagnostic accuracy of 98.79% and 98.36%. The diagnostic accuracy is improved by 15.7% and 22.48%, which indicates that the model has strong anti-noise ability. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Computational Mechanics / Jisuan Lixue Xuebao is the property of Chinese Journal of Computational Mechanics Editorial Office, Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ball bearings are one of the most critical components of rotating machines. They ensure shaft support and friction reduction, thus their malfunctioning directly affects the machine's performance. As a consequence, it is necessary to monitor the health conditions of such a component to avoid major degradations which could permanently damage the entire machine. In this context, HMS (Health Monitoring Systems) and PHM (Prognosis and Health Monitoring) methodologies propose a wide range of algorithms for bearing diagnosis and prognosis. The present article proposes an end-to-end PHM approach for ball bearing RUL (Remaining Useful Life) estimation. The proposed methodology is composed of three main steps: HI (Health Indicator) construction, bearing diagnosis and RUL estimation. The HI is obtained by processing non-stationary vibration data with the MODWPT (Maximum Overlap Discrete Wavelet Packet Transform). After that, a degradation profile is defined and coupled with crack initiation and crack propagation fatigue models. Lastly, a MB-HMM (Hidden Markov Model) is trained to capture the bearing degradation dynamics. This latter model is used to estimate the current degradation state as well as the RUL. The obtained results show good RUL prediction capabilities. In particular, the fatigue models allowed a reduction of the ML (Machine Learning) model size, improving the algorithms training phase. [ABSTRACT FROM AUTHOR]

The possibility of using water-based lubricants in bearings is examined. An overview of the different types of water-based lubricants, their composition and properties, and methods of their application in bearings are provided. Particular attention is paid to studies showing the effectiveness of water-based lubricants in reducing friction and wear and increasing the energy efficiency of bearings. The research results confirm the potential of water-based lubricants for use in various industrial sectors where high performance and reduced environmental impact are required. Valuable information for engineers and manufacturers seeking to improve bearing performance and reduce the environmental impact is provided. [ABSTRACT FROM AUTHOR]

A proposed low-temperature forging method is presented to enhance stainless steel bearings by creating a martensitic subsurface layer, significantly boosting bearing fatigue life due to increased surface hardness. This technique induces beneficial residual stresses, particularly in axial bearings, streamlining their construction and improving machine elements. Challenges persist, especially with radial bearings, but simplicity in axial bearing forging promotes compact, resource-efficient facility construction. Future research will focus on applying this technique to axial bearing washers, potentially replicating success in other bearing components. Despite the energy expenditure on cooling during forging, the substantial increase in bearing fatigue life offsets this, enhancing overall durability and reliability of critical machine components. Integration of this forging technique into bearing fabrication appears seamless, offering a promising trade-off between energy use and enhanced performance. [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]

Monoblock ceramic cups are designed to accommodate large-diameter femoral heads. This has the potential to offer the advantages of an increased range of motion and enhanced joint stability. These features could benefit younger and high-demand patients in need of total hip arthroplasty. The aim of this study was to assess the survival rate and the reasons for revision of the DeltaMotion cup. Data from the AOANJRR were analyzed for all patients who had undergone a primary conventional THA performed between January 1, 2001 and December 31, 2021. Only prostheses with ceramic/ceramic, ceramic/XLPE, metal/XLPE, or CM/XLPE bearing surfaces were included. The primary outcome measure was the cumulative percent revision for all causes. Secondary outcome measures were revision for dislocation/instability, ceramic breakage, or noise. A subanalysis for cup size was also performed. There were 486,946 primary conventional THA procedures undertaken for any reason. Of these, 4,033 used the DeltaMotion cup and 482,913 were modular designs. The DeltaMotion cup had the lowest CPR for all diagnoses compared to the modular bearings at all time points, had a significantly lower revision rate for prosthesis dislocation and no revisions for squeaking compared to other modular bearings. There were 175 ceramic breakages recorded in the modular bearing group and 1 ceramic breakage in the DeltaMotion group. The DeltaMotion cup had a low rate of all-cause revision, and for dislocation, ceramic breakage, and noise. Although this cup is no longer manufactured, ongoing follow-up of newer monoblock ceramic cups will determine their suitability for younger and more active patients. [ABSTRACT FROM AUTHOR]

Rolling mill bearings are prone to wear, erosion, and other damage characteristics due to prolonged exposure to rolling forces. Therefore, regular inspection of rolling mill bearings is necessary. Ultrasonic technology, due to its non-destructive nature, allows for measuring the oil film thickness distribution within the bearing during disassembly. However, during the process of using ultrasonic reflection coefficients to determine the oil film thickness and distribution state of rolling mill bearings, changes in bearing temperature due to prolonged operation can occur. Ultrasonic waves are susceptible to temperature variations, and different temperatures of the measured structure can lead to changes in measurement results, ultimately distorting the results. This paper proposes using density and sound speed compensation methods to address this issue. It simulates and analyzes the oil film reflection coefficients at different temperatures, ultimately confirming the feasibility and effectiveness of this approach. The paper establishes a functional relationship between bearing pressure and reflection coefficients, oil film thickness, and reflection coefficients. This allows for the compensation of reflection coefficients under any pressure conditions, enhancing the accuracy of oil film thickness detection. The proposed method provides technical support for the maintenance of plate rolling processes in the steel industry. [ABSTRACT FROM AUTHOR]

Sensor-based monitoring of bridges has the potential to be an important tool to supplement visual inspection. Monitoring can provide quantitative data to evaluate the condition of bridge components (e.g. bearings and expansion joints) and to inform operation and maintenance decisions. However, the use of sensor systems to monitor bridges is often limited by cost. This paper presents the design, development, and field implementation of a low-cost micro-electromechanical systems (MEMS) and Internet of things (IoT)-based system to measure bridge bearing movement. The developed system uses accelerometers and converts changes in gravitational acceleration to longitudinal bearing displacement. The monitoring system uses a hybrid wired/wireless approach, in which the sensing nodes are wired to a gateway node from which data is transmitted to the cloud. Power is provided by means of a single battery that is charged using a solar panel. To evaluate the system performance in the field, it was installed on the Waaban Crossing in Kingston, Canada. Results of the study showed that the proposed system was capable of measuring movement of the bridge at a cost that was significantly less than a commercial monitoring system. Limitations of the system, cost of installation, and calibration of the sensors are also discussed. [ABSTRACT FROM AUTHOR]

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Thi study evalautes a new bone-preserving femoral head cover that mimics the articular cartilage of the femoral head. A specially developed polyurethane (PU) was evaluated in biocompatibility (cytotoxicity test) and mechanical response to tensile loading. In the cytotoxicity test, steam sterilized (SS) and ethylene oxide sterilized (EtO) PU samples were incubated separately in a cell culture medium. The seeded cell line MG-63 was then added to these sample-incubated cell culture mediums. One negative control group and one positive control group were also evaluated. The cells in each group were cultured for seven days before being quantified using the alamarBlue assay. In the mechanical test, the femoral head cover implants were separated into three groups of three samples. Each group represented a different implant insertion idea: direct insertion (uc sample) and another two insertion modes (is and ss samples) representing implants with enclosure mechanisms. The test consisted of distance-controlled cyclic tensile loadings followed by a failure test. The cytotoxicity test results show no significant difference in fluorescence intensity between the negative control, the three SS groups, and one EtO group (P > 0.05). However, the other two EtO groups exhibit significantly lower fluorescence intensity compared with the negative control (P < 0.05). In the mechanical test, the is samples have the highest cyclic loading force at 559.50 ± 51.41 N, while the uc samples exhibit the highest force in the failure test at 632.16 ± 50.55 N. There are no significant differences (P > 0.05) among the uc, is, and ss groups in terms of stiffness. The cytotoxicity test and the mechanical experiment provide initial assessments of the proposed PU femoral head cover implant. The evaluation outcomes of this study could serve as a foundation for developing more functional design and testing methods, utilizing numerical simulations, and developing animal/clinical trials in the future. • Introduce a conceptual idea and design of a femoral head implant aimed to preserve the bone and mimic the articular cartilage. • The implant is made of a specially developed polyurethane (PU), creating a compliant/cushioning bearing to the hip. • The cytotoxicity test was performed to verify if the implant material is toxic to the cell after sterilization. • The mechanical tensile test was performed to evaluate the implants' response to load and the implementation concepts. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of this study was to assess 10-year patient-reported outcome measures, complications, polyethylene wear-rates, and implant survivorships in patients ≤30 years of age treated with contemporary total hip arthroplasty (THA). We retrospectively assessed 121 patients (144 hips) who underwent THA at age ≤30 years (mean 23 [range, 11 to 30]) at an average follow-up duration of 10.7 years (range, 8 to 17). Highly-crosslinked polyethylene acetabular liners were used in all cases. Femoral heads were ceramic (74%) or cobalt-chrome (26%). There were 52 hips (36%) that had previous surgery and 31 hips (22%) were in patients who had associated major systemic comorbidities. We analyzed the modified Harris Hip scores, University of California Los Angeles Activity Scores, major complications, polyethylene wear-rates, and implant survivorships. At final follow-up, the average modified Harris Hip scores improved from 47 (±15.1) to 81 (±19.5) with an average 34-point improvement. The University of California Los Angeles scores improved from 4.0 (±2.3) to 6.0 (±2.4). The major complication rate was 5.6%. There were 6 hips (4.2%) that were revised. Indications for revision included instability (3, 2.1%), late infection (1, 0.7%), liner dissociation (1, 0.7%), and acetabular loosening (1, 0.7%). Mean linear (0.0438 mm/y) and volumetric (29.07 mm3/y) wear rates were low. No periprosthetic osteolysis was detected in any hip. Survivorship free from revision for any reason was 97.2, 95.8, and 95.8% at 5, 10, and 15 years. Contemporary THA in patients ≤30 years of age is associated with marked clinical improvements at 10-year follow-up and encouraging survivorship estimates at 15 years. [ABSTRACT FROM AUTHOR]

With the advancement of industries such as high-speed railways, new energy vehicles, and wind power, bearings are frequently exposed to various electric field environments, leading to the need for lubricating oil films of bearings to withstand voltage. One of the major issues caused by the breakdown discharge process of the lubricating oil film in bearings is the generation of local instantaneous high temperatures. This temperature rise is a key factor contributing to problems such as high operating temperature of bearings, surface damage in the contact area, and degradation of lubrication performance. This research article focuses on the comprehensive influence of bearing friction and electrical factors. It establishes a heat source calculation model and a temperature field simulation model specifically for current-carrying bearings. This study analyzes both the overall temperature rise of bearings and the local temperature rise resulting from breakdown discharge. Furthermore, the accuracy of the simulation analysis is verified through experiments. The temperature field simulation and experimental results consistently indicate that electrical environmental factors can cause an increase in the overall temperature rise of a bearing. Additionally, the breakdown and discharge of the lubricating oil film generate local instantaneous high temperatures in the contact area of the bearing. [ABSTRACT FROM AUTHOR]

Featured Application: The research results enable the rapid and accurate detection of surface defects in the bearing-manufacturing process. As a critical foundational component, bearings find widespread application in various mechanical equipment. In order to achieve automated defect detection in the bearing-manufacturing process, a defect detection algorithm combining magnetic particle inspection with deep learning is proposed. Dynamic thresholding and generative adversarial network (GAN) methods are employed to extract defect samples from bearing images and augment the dataset, thereby enhancing data diversity. To mitigate the impact of irrelevant displays in bearing images, a coordinated attention (CA) mechanism is introduced into the backbone network of the deep learning model to focus on key information. Additionally, an adaptive spatial feature fusion module (ASFF) is incorporated during the multiscale fusion stage to maintain consistency in features across different hierarchical levels. The weighted intersection over union (WIoU) bounding box loss function is utilized to replace the original generalized intersection over union (GIoU) in the network, directing the model's attention towards common-quality anchor boxes to reduce the adverse effects of inconsistent annotations. The experimental results demonstrate that the improved network achieves a mean average precision (mAP) of 98.4% on the bearing dataset, representing a 4.2% improvement over the original network. [ABSTRACT FROM AUTHOR]

The detection of faulty bearings is an essential step in guaranteeing the safe and efficient operation of rotating machinery. Bearings, which also transmit the loads and pressures generated by the machinery, support the rotating shafts. A common method for bearing fault diagnostics is using signal processing techniques. In terms of accuracy, dependability, and sensitivity to various fault types and severity levels, these techniques do, however, have significant limits. To address these limitations, practitioners often integrate signal processing with advanced techniques like machine learning and data analytics to enhance diagnostic accuracy, reliability, and overall effectiveness in bearing fault detection and predictive maintenance. Exploration of various models has demonstrated enhanced results in managing nonlinear data to a certain degree; however, these models face challenges when dealing with intricately complex patterns. Moreover, CNNs can automatically learn relevant features from raw sensor data, capturing intricate patterns and relationships in the data without the need for manual feature engineering. CNNs can be optimized for scalability and real-time processing, essential for applications requiring quick decisions and computationally less expensive for large datasets compared to other models. An optimized one-dimensional Convolutional Neural Network (1D CNN) using different kernel sizes is proposed for predicting and finding bearing problems to overcome these constraints. This method creates a feature vector by applying many filters of various sizes to the input signal. Using the created feature vector, the input signal can be divided into many categories, such as healthy or unhealthy. In comparison to other methods, the proposed technology performs better and offers a high accuracy of 99.52% in bearing fault identification. The 1D CNN model with multiple kernel sizes excels in preserving data structure during dimensionality reduction, as confirmed by comparing t-Distributed Stochastic Neighbor Embedding plots. Particularly, the optimized 1D CNN with multiple kernel sizes accurately classifies faults with minimal errors, showcasing its fault classification proficiency compared with the other state-of-the-art methods. The visualization underscores the methodology’s efficacy in discerning intricate fault patterns within the data.

The increasing complexity of modern mechanical systems, especially rotating machinery, demands effective condition monitoring techniques, particularly deep learning, to predict potential failures in a timely manner and enable preventative maintenance strategies. Health monitoring data analysis, a widely used approach, faces challenges due to data randomness and interpretation difficulties, highlighting the importance of robust data quality analysis for reliable monitoring. This paper presents a two-part approach to address these challenges. The first part focuses on comprehensive data preprocessing using only feature scaling and selection via random forest (RF) algorithm, streamlining the process by minimizing human intervention while managing data complexity. The second part introduces a Recurrent Expansion Network (RexNet) composed of multiple layers built on recursive expansion theories from multi-model deep learning. Unlike traditional Rex architectures, this unified framework allows fine tuning of RexNet hyperparameters, simplifying their application. By combining data quality analysis with RexNet, this methodology explores multi-model behaviors and deeper interactions between dependent (e.g., health and condition indicators) and independent variables (e.g., Remaining Useful Life (RUL)), offering richer insights than conventional methods. Both RF and RexNet undergo hyperparameter optimization using Bayesian methods under variability reduction (i.e., standard deviation) of residuals, allowing the algorithms to reach optimal solutions and enabling fair comparisons with state-of-the-art approaches. Applied to high-speed bearings using a large wind turbine dataset, this approach achieves a coefficient of determination of 0.9504, enhancing RUL prediction. This allows for more precise maintenance scheduling from imperfect predictions, reducing downtime and operational costs while improving system reliability under varying conditions.

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.

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]

Highly cross-linked polyethylene (HXLPE) acetabular bearing surfaces have appeared to offer excellent wear resistance, low incidence of wear-related osteolysis, and high implant survivorship at 10-year to 15-year follow-up. However, concerns over potential performance deterioration at longer-term follow-up remain — particularly in younger patients — and outcome data into the third decade have not been available. We retrospectively assessed 62 patients (68 hips) who underwent primary total hip arthroplasty (THA) at age ≤50 years with a single manufacturer's cementless components, remelted HXLPE liner, and small diameter (26 and 28 millimeter) cobalt-chromium (CoCr) femoral heads at minimum 18-year follow-up. We assessed clinical outcomes (modified Harris Hip score, University of California Los Angeles Activity Score, polyethylene wear rates, radiographic findings (osteolysis, component loosening), and implant survivorship. At 20.6-year mean follow-up (range, 18 to 23 years) modified Harris Hip scores for surviving hips remained an average of 41 points above preoperative baseline (49 versus 90, P <.001) and UCLA scores 2.8 points above baseline (3.7 versus 6.4, P <.001). Wear analysis revealed a population linear wear rate of 0.0142 mm/y (standard deviation (SD), 0.0471) and volumetric wear rate of 10.14 mm3/y (SD, 23.41). Acetabular lysis was noted in 2 asymptomatic hips at 16.6 and 18.4 years. No components were radiographically loose. Survivorship free from wear-related revision was 100% at 20 years (97% free from any revision). The HXLPE-CoCr bearing couple with small femoral heads continues to be extremely effective 20 years after primary THA in the younger patient population. [ABSTRACT FROM AUTHOR]