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The article describes the use of magnets, paper clips and a ball bearing in teaching molecular geometries. The materials can be used to demonstrate how the valence-shell electron air repulsion model (VSEPR) may be applied to predict molecular geometries. The practical advantages of using magnetic models than other models include the easy addition or removal of magnets in comparing domain geometry and molecular geometry.

Multiphysics models were used to simulate the deformational characteristics experienced by paper under metering blades. Paper is found to slip microscopically at the backing roll interface and subsequently buckle either side of the blade during metering operations. Micro-slippage of the paper at the interface is a linear function of the separation height incurred during buckling. The effects of altering the static coefficient of friction as well as the properties of the backing roll material are studied. Increasing the coefficient of static friction reduces the extent of buckling while a non-linear relationship is found to arise on increasing the elastic modulus of the backing roll material. The mechanisms involved in micro-buckling deformation are explicated herein. [Copyright &y& Elsevier]

As a key mechanism of belt conveyor, the health status and working state of its parts have a profound impact on whether the belt conveyor can run normally and safely. In the composition of the standard belt conveyor, the number of rollers is numerous and scattered. At the same time, under the complex environment of the work site, the fault detection of each roller is particularly difficult. In order to solve the above problems, a diagnosis method based on thermal infrared image features is proposed to detect the faults of each roller mechanism in the belt conveyor. Firstly, the position of the idler is identified based on the YOLOv4 identification method, and then the sticking resistance and bearing damage of the idler are detected based on the temperature difference discrimination method. In this paper, the target recognition method based on YOLOv4 is used to identify the position of the roller, and the recognition accuracy is 93.8%, which meets the requirements of the project. The infrared image obtained by the dual-spectrum camera is used to distinguish the fault of the idler in the coal mine. The temperature of the bearing and surface of the normal roller increases rapidly within 10 minutes of operation, and the temperature changes slightly after 10 minutes of operation. The bearing damaged idler has a greater friction effect at the bearing, so the temperature at the bearing rises faster, and there is a temperature difference of about 7°C between the bearing and the normal roller. The surface temperature of the idler in the blocking state is also fast for about 20 minutes, and there will be a temperature difference of about 8°between the surface of the idler and the normal roller. In this paper, it is determined that the temperature rise coefficients of the roller surface and bearing under normal conditions are 24% 28% and 18% 22% respectively. It is determined that the threshold value of the temperature rise coefficient in the blocking state and the damaged state is 30% and 25% respectively, that is, when the surface temperature rise coefficient of the roller is detected to be more than 30%, it is determined that the card resistance fault occurs, when the temperature rise coefficient at the roller bearing is detected > 25%, the bearing damage fault is judged. [ABSTRACT FROM AUTHOR]

Bearings are one of the critical components of rotating machinery, and their failure can cause catastrophic consequences. In this regard, previous studies have proposed a variety of intelligent diagnosis methods. Most existing bearing fault diagnosis methods implicitly assume that the training and test sets are from the same distribution. However, in real scenarios, bearings have been working in complex and changeable working environments for a long time. The data during their working processes and the data used for model training cannot meet this condition. This paper proposes an improved adversarial transfer network for fault diagnosis under variable working conditions. Specifically, this paper combines an adversarial transfer network with a short-time Fourier transform to obtain satisfactory results with the lighter network. Then, this paper employs a channel attention module to enhance feature fusion. Moreover, this paper designs a novel domain discrepancy hybrid metric loss to improve model transfer learning performance. Finally, this paper verifies the method's effectiveness on three datasets, including dual-rotor, a Case Western Reserve University dataset and the Ottawa dataset. The proposed method achieves average accuracy, surpassing other methods, and shows better domain alignment capabilities. [ABSTRACT FROM AUTHOR]

Wind turbine energy generators operate in a variety of environments and often under harsh operational conditions, which can result in the mechanical failure of wind turbines. In order to ensure the efficient operation of wind turbines, the detection of any abnormality in the mechanics is particularly important. In this paper, a method for detecting abnormalities in the bearings of wind turbine energy generators, based on the cascade deep learning model, is proposed. First, data on the mechanics of wind turbine generators were collected, and the correlation between the data was studied in order to select the parameters related to the bearing temperature. Then, the logical relationship between the observation parameters and the target parameters was established based on a one-dimensional convolutional neural network (CNN) and a long short-term memory (LSTM) network, and the difference between the predicted temperature and the actual temperature was assessed using the root mean square error evaluation model. Finally, a numerical example was used to verify the operational data from a wind farm unit in northwest China. The results show that the CNN-LSTM model proposed in this paper can detect abnormalities earlier in the state of the main bearing than the LSTM model, and the CNN-LSTM model can detect abnormalities in the main bearing that the LSTM network cannot find. [ABSTRACT FROM AUTHOR]

The bearing is the core component of the gearbox transmission system. Once it is damaged during operation, it will cause the shutdown of the mechanical equipment for maintenance. It has important application significance to carry out fault detection and remaining useful life (RUL) prediction. Whereas, some bottlenecks, such as the noise interference of state characteristics, the excessive dependence of supervised learning on prior samples, and the practical RUL online calculation, restrict the industrial application of RUL prediction for rotating machinery equipment. To overcome the above problems, this paper introduces the discrete wavelet transform (DWT) to decrease the noise of the vibration acceleration signal obtained, and then uses the sliding average method to weaken the transient excitation. To make the state characteristics of the monitored bearing trendy, linear, and monotonic, this paper proposes a new set of state interpret indicators: energy and cumulative summation feature (CSF) to reflect the bearing health status. Based on the available bearing health information, the fault boundary threshold is established through the 3 σ criteria, which serves as the basis for first predicting time (FPT) detection. Once the FPT point is determined, this paper applies CSF to replace the original vibration acceleration amplitude as the degradation indicator, which has better linearity and monotonicity than amplitude-based indicators, and which is conducive to the implementation of simple structure curve fitting to carry out the overall RUL prediction. Comparing with existing methods, such as relevance vector machine (RVM), deep belief network (DBN), and particle filtering (PF)-based methods, the experimental results demonstrate that the proposed method has the best RUL prediction efficiency and the fastest convergence. [ABSTRACT FROM AUTHOR]

Due to their unique structural design, portal cranes have been extensively utilized in bulk cargo and container terminals. The bearing fault of their drive motors is a critical issue that significantly impacts their operational efficiency. Moreover, the problem of imbalanced fault samples has a more pronounced influence on the application of novel fault diagnosis methods. To address this, the paper presents a new method called bidirectional gated recurrent domain adversarial transfer learning (BRDATL), specifically designed for imbalanced samples from portal cranes' drive motor bearings. Initially, a bidirectional gated recurrent unit (Bi-GRU) is used as a feature extractor within the network to comprehensively extract features from both source and target domains. Building on this, a new Correlation Maximum Mean Discrepancy (CAMMD) method, integrating both Correlation Alignment (CORAL) and Maximum Mean Discrepancy (MMD), is proposed to guide the feature generator in providing domain-invariant features. Considering the real-time data characteristics of portal crane drive motor bearings, we adjusted the CWRU and XJTU-SY bearing datasets and conducted comparative experiments. The experimental results show that the accuracy of the proposed method is up to 99.5%, which is obviously higher than other methods. The presented fault diagnosis model provides a practical and theoretical framework for diagnosing faults in portal cranes' field operation environments. [ABSTRACT FROM AUTHOR]

The automotive industry requires effective evaluation methods for the quality control of automobile parts and bearings. The ISO standard defines the calculation of roughness parameters from the material ratio curve ("MRC") and material ratio curve on normal probability paper ("MPC") as effective methods for evaluating surfaces with excellent lubrication and frictional characteristics. ISO 4287 specifies the slice method as a calculation method for the MRC. The analysis time of the slice method is long due to the large amount of calculation required. Therefore, ISO 21920-2 specifies the use of the sort method. The sort method reduces the analysis time significantly due to the small amount of calculation required. A previous study revealed that errors occur in the MRC using the sort method compared to the slice method. However, the previous study concluded that the errors were acceptable compared to the time cost. In addition, the plateau surface is a surface with excellent sliding properties. The roughness parameters of the plateau surface have to be calculated from the MPC. However, in the case of expression on normal probability paper, the difference between MRCs calculated by the sort and slice methods increases as both ends approach. Therefore, the results of roughness parameters calculated by each MPC are expected to be different. This study reports the results of an investigation into the effect that increasing differences have on the roughness parameters. We aim to contribute to the establishment of a highly effective evaluation method by verifying the validity of using the sort method in the calculation of the MPC. [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]

In recent years, most research on bearing fault diagnosis has assumed that the source domain and target domain data come from the same machine. The differences in equipment lead to a decrease in diagnostic accuracy. To address this issue, unsupervised domain adaptation techniques have been introduced. However, most cross-device fault diagnosis models overlook the discriminative information under the marginal distribution, which restricts the performance of the models. In this paper, we propose a bearing fault diagnosis method based on envelope spectrum and conditional metric learning. First, envelope spectral analysis is used to extract frequency domain features. Then, to fully utilize the discriminative information from the label distribution, we construct a deep Siamese convolutional neural network based on conditional metric learning to eliminate the data distribution differences and extract common features from the source and target domain data. Finally, dynamic weighting factors are employed to improve the convergence performance of the model and optimize the training process. Experimental analysis is conducted on 12 cross-device tasks and compared with other relevant methods. The results show that the proposed method achieves the best performance on all three evaluation metrics. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of mitigation approaches, with a specific focus on geometric modifications, to mitigate shaft voltage and bearing current in electric drives. While existing literature extensively covers inverter-based and coupling path-based methods, the potential of geometric modifications has been relatively underexplored. The geometric modification approach offers a cost-effective and weight-saving solution that can be incorporated during the early design phase of the machine. The review begins by outlining various mitigation techniques applied at the victim machine, including bearing insulation, ceramic ball bearings, shaft grounding, and more. Subsequently, it delves into the machine geometric modification approach, examining methods related to winding design, stator and rotor iron geometry, rotor surface insulation, and stator slot wedge modifications. The paper highlights the practical relevance of geometric modifications, emphasizing their advantages over conventional methods. The review paper aims to shed new light on the potential of geometric modifications in mitigating shaft voltage and bearing current in electric drives. By providing valuable insights into this approach, researchers and engineers can develop robust and efficient electric drive systems, advancing the field of electrical machine design and operation. [ABSTRACT FROM AUTHOR]

Aiming to resolve the problem where the reliability of gearbox bearings of wind turbines is easily affected by random impact, this paper puts forward a reliability evaluation method for the competitive failure of gearbox bearings of wind turbines considering the fault incubation point. Firstly, we use Weibull distribution to simulate the fault latent process of wind turbine gearbox bearings. Secondly, a natural degradation model of gearbox bearing is established based on the Wiener process. Then, we model the random impact arrival frequency and impact intensity through a homogeneous Poisson process and normal distribution, respectively. Finally, based on considering the fault incubation point, the natural degradation of the bearing, the instantaneous degradation caused by impact, and the decline in the impact resistance of the bearing, a reliability evaluation model of gearbox bearings of wind turbines is established. A high-speed bearing of a gearbox from a wind farm in northern China is selected for simulation analysis. The results show that the proposed method can better describe the reliability decline process of the gearbox bearings of wind turbines, which has a specific guiding significance for the maintenance of wind turbines. [ABSTRACT FROM AUTHOR]

Dynamic coefficients of a bearing are basic elements of rotor dynamics analysis. At present, there are still some issues in the calculation of the dynamic coefficient of air foil bearing. In this paper, the dynamic coefficients of the air foil bearing are calculated by time-domain identification. This method does not need to linearize the system equations, so it is generally applicable to different models for air foil bearings. Using the established method, this paper verified the calculation results using the foil model with axial uniform deformation for the first time, and the influence of the foil model on the dynamic coefficients was studied. The calculation results of the foil models with axial uniform deformation and axial independent deformation have significant differences. Furthermore, the accuracy of the dynamic coefficient method for different disturbance amplitudes was compared. The results indicate that the dynamic coefficient method has good accuracy over a large disturbance range. [ABSTRACT FROM AUTHOR]

The prognosis of wind turbine failures in real operating conditions is a significant gap in the academic literature and is essential for achieving viable performance parameters for the operation and maintenance of these machines, especially those located offshore. This paper presents a framework for estimating the remaining useful life (RUL) of the main bearing using regression models fed operational data (temperature, wind speed, and the active power of the network) collected by a supervisory control and data acquisition (SCADA) system. The framework begins with a careful data filtering process, followed by creating a degradation profile based on identifying the behavior of temperature time series. It also uses a cross-validation strategy to mitigate data scarcity and increase model robustness by combining subsets of data from different available turbines. Support vector, gradient boosting, random forest, and extra trees models were created, which, in the tests, showed an average of 20 days in estimating the remaining useful life and presented mean absolute error (MAE) values of 0.047 and mean squared errors (MSE) of 0.012. As its main contributions, this work proposes (i) a robust and effective regression modeling method for estimating RUL based on temperature and (ii) an approach for dealing with a lack of data, a common problem in wind turbine operation. The results demonstrate the potential of using these forecasts to support the decision making of the teams responsible for operating and maintaining wind farms. [ABSTRACT FROM AUTHOR]

In this paper, the vibration control of the multivariable model of rotor bearing systems is considered for investigation. Some simply structured controllers that can suppress vibrational disturbances are tested for their robustness via the H∞ optimality criteria. Initially, intelligent optimisation techniques are used to minimize the H∞ mixed-sensitivity norm of the Linear Fractional Transformation (LFT) of the simple two-term PI controllers acting on the rotor system models. This results in some controllers that can suppress the vibration but with a slow oscillatory response. After this, an appropriate interpretation of the Bode plot singular values of the combined sensitivity and control effort matrix is used to explain the performance shortcomings of this controller. Moreover, the existing simply structured controllers in the literature exhibiting a faster performance are examined by using singular value plots. It is shown that when the maximum singular value of the control effort matrix drops below the 0 db line, the performance will be boosted. Finally, the H∞ controllers are designed by using the robust control toolbox in MATLAB. This resulted in rapid disturbance rejection, with the vibration amplitude diminishing to zero after 0.3 s due to double-step disturbances. However, these controllers in the frequency domain have an order of eight and may not be realizable to be implemented in practice. It is concluded that examining the Bode plot of the maximum singular value of the control effort matrix is a useful tool for evaluating performance in the frequency domain. However, designing robust controllers by toolboxes in the time domain can lead to superb performance with higher-order controllers. [ABSTRACT FROM AUTHOR]

In recent years, the trend towards larger wind turbines and higher power densities has led to increasing demands on planet gear bearings. The use of sliding bearings instead of rolling bearings in planetary bearings makes it possible to increase the power density with lower component costs and higher reliability. Therefore, the use of planet gear sliding bearings in wind turbine gearboxes has become more common. However, the flexible structure and complex load conditions from the helical tooth meshes lead to highly complex elastic structure deformation that modifies the lubricant film thickness and pressure distribution and, thus, has to be considered in the calculation of the bearing's load-carrying capacity. This paper introduces a highly time-efficient calculation procedure that is validated with pressure measurement data from a three-stage planetary gearbox for a multi-megawatt wind energy plant. The investigations focus on three main objectives: (i) analyses of experimental and predicted results for different load cases, (ii) validation of the results of planet gear sliding bearing code, and (iii) discussion on mandatory modeling depths for the different planet stages. Results indicate the necessity of further research in this field of applications, particularly for the third-stage bearings. [ABSTRACT FROM AUTHOR]

This paper studies the rating lives of wind turbine main bearings, as determined by the IEC 61400‐1 and ISO 281 standards. A critical review of relevant bearing life theory and turbine design requirements is provided, including discussion on possible shortcomings such as the existence (or not) of the bearing fatigue load limit and the validity of assuming linear damage accumulation. A detailed exploratory case study is then undertaken to determine rating lives for two models of main bearing in a 1.5 MW wind turbine. Rating life assessment is carried out under different conditions, including various combinations of main bearing temperature, wind field characteristics, lubricant viscosity, and contamination levels. Rating lives are found to be sufficiently above the desired 20‐year design life for both bearing models under expected operating conditions. For the larger bearing, operational loads are shown to be below or close to the bearing fatigue load limit a vast majority of the time. Key sensitivities for rating life values are temperature and contamination. Overall, the results of this study suggest that an ISO 281 rating life assessment does not account for reported rates of main bearing failures in 1 to 3 MW wind turbines. It is recommended that a similar analysis be undertaken for ISO/TS 16281 rating lives, along with further efforts to identify principal root causes of main bearing failures in future work, possibly leading to a new application standard specific to this component. It is also recommended that the impacts of partial wake impingement on main bearing rating lives are investigated. [ABSTRACT FROM AUTHOR]

The application of the existing complex network in fault diagnosis is usually modelled based on the time domain, resulting in the loss of sign frequency-domain features, and the extracted topology features of network are too macroscopic and insensitive to local changes within the network. This paper proposes a new method of local feature extraction based on frequency complex network (FCN) decomposition and builds a new complex network structure feature on this basis, namely, subnetwork average degree. The variation law of signals in frequency domain is obtained with the aid of the structural features of complex network. The local features that are sensitive to local changes of the network are applied to characterize the whole network, with flexible application and without limitation in mechanism. The average degree of subnetwork could be regarded as feature parameters for rolling bearing fault diagnosis and degradation state recognition. Analysis on the experimental data and bearing life cycle data shows that the method proposed in this paper is effective, revealing that the extracted features have effective separability and high accuracy in fault recognition and the degradation detection of the life cycle of rolling bearings combined with neural networks. Moreover, the proposed method has reference value for the processing and recognition of other nonstationary signals. [ABSTRACT FROM AUTHOR]

The lubrication of the mechanical components reduces friction, and increases the efficiency and the reliability. However, the interaction of moving components with the lubricant leads to power losses due to viscous and inertial effects. Nowadays, the study of lubricant behavior can be carried out through computational fluid dynamics (CFD) simulations. Nevertheless, the modeling of the computational domain within complex mechanical systems (e.g., ordinary, planetary and cycloidal gearboxes, roller bearings, and pumps) requires the exploitation of specific CFD techniques. In the last decades, many mesh-based or meshless approaches have been developed to deal with the complex management of the topological changes of the computational domain or the modeling of complex kinematics. This paper aims to collect and to classify the scientific literature where these approaches have been exploited for the study of lubricated mechanical systems. The goal of this research is to shed a light on the current state of the art in performing CFD analysis of these systems. Moreover, the objective of this study is to stress the limits and the capabilities of the main CFD techniques applied in this field of research. Results show the main differences in terms of accuracy achievable and the level of complexity that can be managed with the different CFD approaches. [ABSTRACT FROM AUTHOR]

Intelligent fault diagnostic systems are fast becoming key instruments in industrial applications. This paper presents a recognition system for diagnosing bearing defects in induction motors. The proposed scheme is comprised of five steps, namely signal segmentation, feature extraction and reduction, fault classification and the decision fusion. First, the vibration signal is segmented into successive equal-length intervals, which are considered as patterns in a recognition problem. The objective is to predict the defect mode (class) for each pattern. Then, the time- and the frequency-domain features are extracted from each interval. At the next step, a small set of distinctive and informative features is found by resorting to different feature reduction techniques to guarantee well-organized learning and immediate and accurate classification. Then, in the fourth step, various classifiers are trained to learn to distinguish between the faulty and healthy states. To make the final decision, different combinations of classifiers are considered using the voting and stacking techniques to enhance the overall performance of the recognition system. Evaluation of the proposed diagnostic scheme on the standard CWRU bearing defect database demonstrates that this system attains reasonable performance measures, validating the ideas put forward in this paper. [ABSTRACT FROM AUTHOR]

Almost all rotating machinery in the industry has bearings as their key building block and most of these machines run 24 × 7. This makes bearing health prediction an active research area for predictive maintenance solutions. Many state of the art Deep Neural Network (DNN) models have been proposed to solve this. However, most of these high performance models are computationally expensive and have high memory requirements. This limits their use to very specific industrial applications with powerful hardwares deployed close the the machinery. In order to bring DNN-based solutions to a potential use in the industry, we need to deploy these models on Microcontroller Units (MCUs) which are cost effective and energy efficient. However, this step is typically neglected in literature as it poses new challenges. The primary concern when inferencing the DNN models on MCUs is the on chip memory of the MCU that has to fit the model, the data and additional code to run the system. Almost all the state of the art models fail this litmus test since they feature too many parameters. In this paper, we show the challenges related to the deployment, review possible solutions and evaluate one of them showing how the deployment can be realized and what steps are needed. The focus is on the steps required for the actual deployment rather than finding the optimal solution. This paper is among the first to show the deployment on MCUs for a predictive maintenance use case. We first analyze the gap between State Of The Art benchmark DNN models for bearing defect classification and the memory constraint of two MCU variants. Additionally, we review options to reduce the model size such as pruning and quantization. Afterwards, we evaluate a solution to deploy the DNN models by pruning them in order to fit them into microcontrollers. Our results show that most models under test can be reduced to fit MCU memory for a maximum loss of 3 % in average accuracy of the pruned models in comparison to the original models. Based on the results, we also discuss which methods are promising and which combination of model and feature work best for the given classification problem. [ABSTRACT FROM AUTHOR]

Flaking failure caused by surface cracks of silicon nitride ceramic bearings has been investigated from the viewpoint of the ring crack model. However, the relation between surface and subsurface cracks under rolling contact fatigue is not fully understood. In this investigation subsurface cracks branching from an initial surface crack were observed in detail, and the process of flaking failure was investigated. The specimens were observed prior to the separation of the surface layers and it was found that the initial surface cracks grew vertically to the surfaces and did not curve as predicted by the ring crack model. Subsurface cracks branched from the single surface cracks and grew in a direction parallel to the surface. They grew in both the same and opposite directions to the ball movement, with small upward and downward branches. These subsurface cracks grew prior to the semi-circular surface cracks. From these observations it was concluded that the flaking failures are not caused directly by surface cracks, but by subsurface cracks that branch from them. [ABSTRACT FROM AUTHOR]

It is important to propose a reasonable nonlinear dynamic model of gas dynamic bearing–rotor system for the stable operation of turbocharger. This paper presents a hybrid gas journal bearing (HGJBs)–rotor system with flexible supporting structure. A fractional-order dynamic model of the symmetrical flexible HGJB–rotor system is established. A tracking–alarming method is proposed to obtain the rotational speed ratio at the bifurcation point. The stable and unstable regions of the rotational speed ratio Ω ω for both the rigid bearing–rotor system and flexible HGJB–rotor system are analyzed. The nonlinear dynamic behaviors of HGJB–rotor system versus the fractional order α d and rotational speed ratio Ω ω are studied. Results show the fractional-order dynamic model can realize the reasonable prediction of nonlinear dynamic behaviors. An increase in the rotational speed ratio Ω ω and the decrease of fractional order α d result in a growth of the vibration amplitude. The adoption of flexible supporting structure is a good strategy to increase the size of stable region. [ABSTRACT FROM AUTHOR]

Wind turbine bearings play a crucial role in ensuring the safe and efficient operation of wind turbines. Accurate estimation of the remaining useful life (RUL) of bearings can significantly reduce operating and maintenance costs. In this paper, we propose three advanced data‐driven models to predict the RUL of high‐speed shaft bearings in wind turbines. These models combine the sparrow search algorithm (SSA) with three different regression models, namely support vector machine, random forest (RF) regression and Gaussian process regression. The models are based on features extracted from the vibration signal analysis, and the features are selected based on their monotonicity to evaluate bearing degradation. To optimize the performance of the regression models, all model parameters are tuned using the SSA algorithm. The proposed models are validated using vibration data collected from a real 2 MW commercial wind turbine. Our results demonstrate that the proposed models are effective in predicting the RUL of wind turbine bearings, and the SSA algorithm improves the accuracy of the predictions. [ABSTRACT FROM AUTHOR]

This study builds upon previous research that utilised a vibration-based machine learning (VML) approach for diagnosing rotor-related faults in rotating machinery. The original method used artificial neural networks (ANN) to classify rotor-related faults based on optimised vibration parameters from the time and frequency domains. This study expands the application of this vibration-based machine learning approach to include the anti-friction bearing faults in addition to the rotor faults. The earlier suggested vibration-based parameters, both in time and frequency domains, are further revised to accommodate bearing-related defects. The study utilises the measured vibration data from a laboratory-scale rotating test rig with different experimentally simulated faults in the rotor and bearings. The proposed VML model is developed for both rotor and bearing defects at a rotor speed that is above the first critical speed. To gauge the robustness of the proposed VML model, it is further tested at two different rotating speeds, one below the first critical speed and the other above the second critical speed. The paper presents the methodology, the rig and measured vibration data, the optimised parameters, and the findings. [ABSTRACT FROM AUTHOR]

• Develop an original FE-analytical model of bolt tightening with elastic of bearing cap. • Propose a new assembly accuracy prediction method of planetary gear train. • Consider gear-shaft-bearing-bolt coupling effects on assembly accuracy. • Explore bolt tightening effects on shaft error motion and assembly accuracy. • Carry out the assembly accuracy and vibration tests to verify the proposed method. Planetary gear train (PGT) is a complex transmission system composed of gears, shafts, bearings and bolted joints, of which the assembly accuracy affects the transmission accuracy and dynamic behavior. In this paper, a new assembly accuracy prediction method is proposed for PGT considering gear-shaft-bearing-bolt coupling effects. The new method is formulated by establishing three models for bolt assembly, shaft error motion and assembly accuracy (transmission error) of PGT. The model for bolt assembly is an original FE-analytical model to analyze the bolt assembly effect on elastic deformation of bearing cap, which is developed by finite element (FE) method and analytical method for addressing the elastic effect of bearing cap and elastic interaction between bolts. The models for shaft error motion and assembly accuracy are performed by the three-dimensional contact analysis with errors and preload of bearing cap based on analytical method and kinematic geometry relationship. Combining the three models, the assembly accuracy can be predicted by simulations. The simulations indicate that the proper bolt assembly parameters can alleviate the influence of manufacturing errors on assembly accuracy. The assembly accuracy tests of PGT are conducted. The results well agree with the simulations', and the assembly accuracy is improved by the proper bolt assembly parameters. [ABSTRACT FROM AUTHOR]

This paper is the second in a two-part study on lubrication in wind turbine main bearings. Where Part 1 provided an introductory review of elastohydrodynamic lubrication theory, this paper will apply those ideas to investigate lubrication in the double-row spherical roller main bearing of a 1.5MW wind turbine. Lubrication is investigated across a "contact conditions dataset" generated by inputting main-bearing applied loads, estimated from hub loads generated using aeroelastic simulation software, into a Hertzian contact model of the main bearing. From the Hertzian model is extracted values of roller load and contact patch dimensions, along with the time rate of change of contact patch dimensions. Also included in the dataset are additional environmental and operational variable values (e.g. wind speeds and shaft rotational speeds). A suitable formula for estimating film thickness within this particular bearing is then identified. Using lubricant properties of a commercially available wind turbine grease, specifically marketed for use in main bearings, an analysis of film thickness across the generated dataset is undertaken. The analysis includes consideration of effects relating to temperature, starvation, grease thickener interactions and possible non-steady effects. Results show that the studied main bearing is at risk of operating under mixed lubrication conditions for a non-negligible proportion of its operational life, indicating that further work is required to better understand lubrication in this context and implications for main-bearing damage and operational lifetimes. Key sensitivities and uncertainties within the analysis are discussed, along with recommendations for future work. [ABSTRACT FROM AUTHOR]

The aim of this paper is to gain insight into the nonlinear vibration feature of a dynamic model of a gas turbine. First, a rod fastening rotor-bearing coupling model with fixed-point rubbing is proposed, where the fractal theory and the finite element method are utilized. For contact analysis, a novel contact force model is introduced in this paper. Meanwhile, the Coulomb model is adopted to expound the friction characteristics. Second, the governing equations of motion of the rotor system are numerically solved, and the nonlinear dynamic characteristics are analyzed in terms of the bifurcation diagram, Poincaré map, and time history. Third, the potential effects provided by contact degree of joint interface, distribution position, and amount of contact layer are discussed in detail. Finally, the contrast analysis between the integral rotor and the rod fastening rotor is conducted under the condition of fixed-point rubbing. [ABSTRACT FROM AUTHOR]

In subtractive manufacturing processes, swarf, burrs or other residues are produced, which can impair the function of a tribological system (e.g., journal bearings). To prevent premature engine damage, cleanliness requirements are defined for production processes. Damaging particle tests are an experimental approach for validating these defined cleanliness requirements. This methodical approach is not yet widely used. For one, the test setup must be developed and proven for the respective application. For another, in order to carry out the tests in a systematic manner, defined test particles with properties similar to those of the contaminants encountered in reality are required. In the second part of the paper, the process chain for manufacturing artificial test swarf by micro powder injection molding (MicroPIM) is described. The size and shape of the swarf were derived from real swarf via several abstraction processes. Although certain design guidelines for MicroPIM parts could not be taken into account, the targeted manufacturing tolerances were achieved in most cases. During demolding, it became apparent that the higher ejection forces of the free-formed geometries must be taken more into account in the design of the mold. The experiments on the test setup also revealed that the artificial test swarf was unexpectedly brittle and was therefore ground up in the bearing gap without causing any substantial damage to the bearing. Thus, the artificial test swarf in its current sintered state is not a suitable substitute for micromilled swarf. However, MicroPIM could still be used to manufacture test particles in applications involving lower mechanical forces. [ABSTRACT FROM AUTHOR]

Improving the bearing design helps in reducing the negative consequences related to errors in installation, manufacturing, deflections under severe loading conditions, progressive wear of machine elements, and many other aspects. One of the methods of such a design improvement effort is changing the bearing profile along the bearing width to compensate for the reduction in the geometrical gap between the shaft and the bearing inner surface due to the aforementioned causes. Since in all rotating machinery, unbalance usually exists at some level, this paper deals with the response of this modified bearing to unbalanced excitation to evaluate the effectiveness of such geometrical design on the dynamic characteristics of the rotor-bearing system. The numerical solution is performed using the finite difference method by assuming Reynolds boundary conditions to determine the cavitation limits, and the 4th-order Range-Kutta method is used to determine the time responses resulting from the unbalance excitation. The time responses to this type of excitation show that the rotor-bearing with the improved geometrical design is more stable, particularly at high speeds. In addition, this modification leads to an improvement in the lubricant layer thickness and the reduction in the levels of the generated pressure between the surfaces despite the presence of large deviations from the perfectly aligned bearing system. Furthermore, the suggested geometrical design overcomes the problem of asymmetricity in the pressure field resulting from the shaft deviation to a large extent. The results of this work (the enhancement in the level of the film thickness and the improvement in the dynamic response of the system as well as the reduction of the maximum pressure value) extend the range of misalignment in which the rotor bearing systems can operate safely which represents a significant step in designing the rotor-bearing system. [ABSTRACT FROM AUTHOR]

With global offshore wind power gradually moving to deep and distant waters, there is a clear trend towards larger-scale units, posing significant challenges for future offshore wind power foundations. In this paper, a helical pile jacket foundation type is proposed by combining the advantages of the current jacket foundation, which is suitable for deep and distant seas and offers high stiffness, with the excellent bearing performance of helical pile foundations. The influence of buried depth on the bearing characteristics of this foundation type is discussed through a physical model test. The results obtained from the study reveal the distinct bearing characteristics and damage modes exhibited by the foundation under deep and shallow burial conditions. These findings clearly indicate that the overall bearing characteristics and damage modes are superior in deep burial conditions compared to shallow burial conditions. Furthermore, it is observed that the damage and displacement of the foundation are more concentrated in localized areas when subjected to shallow burial. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the fracture accident of a cylindrical roller bearing cage used in a charging pump in a nuclear power plant. The causes and mechanisms of bearing cage breakage were investigated by material failure analysis and simulation calculations. Macroscopic observation results confirmed that the cage fracture occurred at the stress concentration position. The microfracture morphology of the cage obtained from scanning electron microscopy showed a fatigue feature. The analysis of residual stress indicated large residual stress perpendicular to the fracture surface. The finite element calculation showed that when the bearing was moving in and out of the working area during operation, large working stress appeared at the stress concentration position. Working stress and residual stress acted together, approaching the fatigue limit of materials, and finally led to the cage fatigue fracture. The stress of the other two structural cages of the same type of bearing was also calculated, and no such large stress concentration was identified; thus, one plastic cage was temporarily used. [ABSTRACT FROM AUTHOR]

In this paper, the authors present a novel construction of an automatic balancing device applicable to balancing shafts working in a heavily polluted environment. The novelty of the presented system lies in the fact that its utilization requires no changes to be made in the already existing shafts. Also, the system is capable of working during the operation of the balanced shaft, so there is no need to stop it. The propulsion system is based on eddy current braking, therefore no wires need to be used in the device. During the development process of the system, three iterations of the device were created. Each iteration is presented, described, and discussed. The advantages and drawbacks of each version are pointed out and explained thoroughly. The correctness of the design was verified by the created devices that were assembled and fixed on shafts to prove the design assumptions. [ABSTRACT FROM AUTHOR]

This paper deals with scale-fullscale models of rotor-bearing systems on rotating condition. The investigation is focused on the gyroscopic effect, which causes forward and backward whirl frequencies. When the rotor-bearing system is scaled proportionally in its dimension (height, length, and width), the scaling factor of whirl frequencies can be derived. It depends on the ratio between a transverse and polar moment of inertia on its rotating axis called the gyroscopic factor. The experimental study is conducted to validate it on three scaled rotor-bearing systems, which shifts its disc from the middle of the shaft on the scale of 1:1, 2:1, and 3:1 to clearly show the gyroscopic effect on first bending natural frequency. The scaling factor is then validated using the Campbell diagram by finding its critical speed. From this critical speed, the whirl frequencies along the range of the full-scale model speed can be obtained. The result also shows that the scaling factor remains the same whether it is at rotation or rest condition. Consideration must be made on the effect of the structural design, that is, blade and support, because of its unsymmetric stiffness that can cause backward whirl frequencies. The bearing stiffness must be ensured to be scaled proportionally, especially on journal bearing cases. This finding can be used by engineers to deal with scaling method implementation on rotating machinery design. [ABSTRACT FROM AUTHOR]

The technology of fault diagnosis is helpful to improve the reliability of wind turbines, and further reduce the operation and maintenance cost at wind farms. However, in reality, wind turbines are not allowed to operate with faults, so few fault samples could be obtained. With a small amount of training data, traditional fault diagnosis models that need huge samples under a deep learning framework are difficult to maintain with high accuracy and effectiveness. Few-shot learning can effectively solve the problem of overfitting caused by fewer fault samples in model training. In view of model-agnostic meta-learning (MAML), this paper proposes a model for few-shot fault diagnosis of the wind turbines drivetrain, which is named model-agnostic meta-baseline (MAMB). The training data is input to the base classification model for pre-training, then, some data is randomly selected from the training set to form multiple meta-learning tasks that are utilized to train the MAML to finally fine-tune the later layers of the model at a smaller learning rate. The proposed model was analyzed by the small samples of the bearing data from Case Western Reserve University (CWRU) data, the generator bearings, and gearboxes vibration data in wind turbines under randomly changing operating conditions. The results verified that the proposed method was superior in one-shot, five-shot, and ten-shot tasks of wind turbines. [ABSTRACT FROM AUTHOR]

High-speed trains often pass through tunnel, turnout, ramp, bridge, and other line features in the process of running. At the same time, the length of the operation time, weather conditions, changes in train running conditions, and other conditions will lead to the loss of the train. In view of the complexity of a high-speed train structure and operation environment, in order to effectively evaluate the health of the train in the operation process, this paper proposes a diagnosis method of bearing temperature anomaly of a high-speed train based on condition identification and multitask deep learning. In this paper, the important components of bogie axle box, gearbox, and traction motor are taken as the research object. Firstly, the operating condition parameters of the high-speed train are analyzed and identified, and the K-means algorithm is used to classify and identify the operating condition of the high-speed train. Then, based on the operating condition identification and multitask deep learning, the bearing temperature prediction model is constructed. In addition, according to statistical quality control theory, the difference between the value predicted by the model and the real value is used to diagnose the anomaly of the bearing temperature of the high-speed train. Finally, the accuracy and availability of the model are verified by an example. The model can judge whether the running train bearing temperature is in the normal range in real time and predict and alarm the abnormal bearing temperature. [ABSTRACT FROM AUTHOR]

In order to further explore and study the influencing factors and regulars of the gas film flow field at the outlet of the orifice of the aerostatic bearing, the large eddy simulation method was used to calculate the N-S equation in the calculation area of the gas film flow field at the outlet of the orifice of the aerostatic bearing, and the overrelaxation iteration method was used to solve the Reynolds equation in the other calculation areas, which was proposed in this paper. Based on the physical model of the outlet of the orifice of the aerostatic bearing, the solution area of the gas film flow field of the aerostatic bearing was solved, and the calculation results were analyzed and discussed. The results showed that there was a sharp drop of the gas film pressure at the outlet of the orifice of the aerostatic bearing and the separation of the gas film pressure p became inconsistent in the thickness direction of gas film clearance h. It was assumed that there was a critical ratio θ between the gas film clearance h and the orifice diameter d of the aerostatic bearing, and when the ratio of gas film clearance h to the orifice diameter d was greater than the critical ratio θ, various fluctuations of the gas film flow field began to appear, which may be the initial signal state of the gas film flow field of the aerostatic bearing starting to transition to turbulence. It provided a theoretical basis and guidance for further study of the transition lubrication mechanism between the laminar and the turbulent flow at the outlet of the orifice of the aerostatic bearing in this paper. [ABSTRACT FROM AUTHOR]

The paper presents the use of the Teager-Kaiser energy operator (TKEO) to evaluate the state of rotor unbalance. The method was developed in 1990 by Kaiser and involves a simple calculation of signal energy. It has been used before in diagnostics, e.g. during the evaluation of instability of hydrodynamic bearings and as a diagnostic symptom of gearbox damage. This paper is the first to present the use of the Teager-Kaiser method to evaluate the rotor unbalance in hydrodynamic bearings. [ABSTRACT FROM AUTHOR]

Gears and bearings are one of the major components of many machines, which can result in operation downtime or even catastrophic failure of a whole system. This paper addresses a tutorial for the features extraction and selection of the gears and bearings, which is known as feature engineering, a prerequisite step for the prognostics and health management (PHM) of these components. While there have been many new developments in this field, no studies have addressed the tutorial aspects of features engineering to aid engineers in solving problems by their own effort, which is of practical importance for successful PHM. The paper aims at helping beginners learn the basic concepts, and implement the algorithms using the public datasets as well as those made by the authors. Matlab codes are provided for them to implement the process by their own hands. [ABSTRACT FROM AUTHOR]

In order to further study the performance of aerostatic bearing, an aerostatic bearing with a multihole integrated restrictor was proposed in this paper. Based on the physical model of aerostatic bearings, the governing equation was established, deduced discretely, and solved numerically by the finite-difference method and flow balance principle in Cartesian coordinates. The bearing capacity and stiffness of aerostatic bearings have been analyzed, and the relevant experimental research has been carried out in this paper. The results showed that the number of orifices in the integrated restrictor had a significant effect on the bearing capacity, and the maximum bearing capacity of a nine-hole integrated throttling bearing was approximately 1.9 times greater than that of a single-hole bearing. The bearing stiffness was greatly affected by the diameter of the orifices, and the optimum bearing stiffness was determined. The corresponding gas film clearance also changed accordingly. [ABSTRACT FROM AUTHOR]

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The health condition of rolling bearing can directly influence to the efficiency and lifecycle of rotating machinery, thus monitoring and diagnosing the faults of rolling bearing is of great importance. Unfortunately, vibration signals of rolling bearing are usually overwhelmed by external noise, so the fault frequencies of rolling bearing cannot be readily obtained. In this paper, an improved feature extraction method called IMFs_PE, which combines the multivariate empirical mode decomposition with the permutation entropy, is proposed to extract fault frequencies from the noisy bearing vibration signals. First, the raw bearing vibration signals are filtered by an optimal band-pass filter determined by SK to remove the irrelative noise which is not in the same frequency band of fault frequencies. Then the filtered signals are processed by the IMFs_PE to get rid of the relative noise which is in the same frequency band of fault frequencies. Finally, a frequency domain condition indicator FFR(Fault Frequency Ratio), which measures the magnitude of fault frequencies in frequency domain, is calculated to compare the effectiveness of the feature extraction methods. The feature extraction method proposed in this paper has advantages of removing both irrelative noise and relative noise over other feature extraction methods. The effectiveness of the proposed method is validated by simulated and experimental bearing signals. And the results are shown that the proposed method outperforms other state of the art algorithms with regards to fault feature extraction of rolling bearing. [ABSTRACT FROM AUTHOR]

Industries rely on the early and efficient detection of bearing faults to enhance the service life of three-phase induction motors. Various bearing health condition monitoring techniques have been proposed for years, majorly involving sound and/or vibration sensors. This paper compares sound- and vibration-based approaches for automated and real-time bearing health diagnosis, evolving a fusion of wavelet scattering transform and a hybrid two-dimensional convolutional neural network architecture with a multi-class support vector machine classifier. In this work, data are collected from our own laboratory-based sample process control application operated through a supervisory control and data acquisition system under harsh industrial environmental conditions, including dynamicity in motor load, speed, and externally induced noise levels from a nearby induction motor. Multiple parameters, such as image resolution, color channel, and intelligent architecture, are optimized for cost-effective real-time fault detection. This study also suggests that the proposed hybrid model, driven by normalized time–frequency feature images, can work with the reduced signal sampling frequency and analog-to-digital converter bit resolution while accomplishing the performance. The robustness of the proposed model is verified on publicly available databases. Rigorously, it is observed that the individual sound and vibration sensor-based measurement systems perform equally well for bearing fault diagnosis with the help of quintessential specification tuning in the data acquisition system. [ABSTRACT FROM AUTHOR]

Wear usually occurs in the loaded part of hydrodynamic bearings and leads to bearing geometry imperfections. This paper investigates the effects of wear-induced geometric imperfections of tilting pad journal bearings (TPJBs) on the dynamic behavior of the system. Furthermore, the effect of wear on the journal-bearing rub-induced contact pressure severity is investigated. A novel tribo-dynamic model is proposed for a flexible rotor-worn TPJB which integrates a mixed elastohydrodynamic model with a rotor-worn TPJB thermal and dynamic model to assess the effects of the bearing wear progression on rotor-TPJB behavior. Based on the results, wear changes the temperature distribution of the pads and oil film as well as the dynamic behavior of the system. Dynamic simulations reveal a higher vibration level and contact pressure for the worn TPJBs near the system's critical speed and service speed. Finally, thermal and dynamic condition indicators are suggested to detect TPJB wear severity at its early stages. [ABSTRACT FROM AUTHOR]

Recently, research concerning electrical machines and drives condition monitoring and fault diagnosis has experienced extraordinarily dynamic activity. The increasing importance of these energy conversion devices and their widespread use in uncountable applications have motivated significant research efforts. This paper presents an analysis of the state of the art in this field. The analyzed contributions were published in most relevant journals and magazines or presented in either specific conferences in the area or more broadly scoped events. [ABSTRACT FROM PUBLISHER]

Mechanical vibration signal mapped into a high-dimensional space tends to exhibit a special distribution and movement characteristics, which can further reveal the dynamic behavior of the original time series. As the most natural representation of high-dimensional data, tensor can preserve the intrinsic structure of the data to the maximum extent. Thus, the tensor decomposition algorithm has broad application prospects in signal processing. High-dimensional tensor can be obtained from a one-dimensional vibration signal by using phase space reconstruction, which is called the tensorization of data. As a new signal decomposition method, tensor-based singular spectrum algorithm (TSSA) fully combines the advantages of phase space reconstruction and tensor decomposition. However, TSSA has some problems, mainly in estimating the rank of tensor and selecting the optimal reconstruction tensor. In this paper, the improved TSSA algorithm based on convex-optimization and permutation entropy (PE) is proposed. Firstly, aiming to accurately estimate the rank of tensor decomposition, this paper presents a convex optimization algorithm using non-convex penalty functions based on singular value decomposition (SVD). Then, PE is employed to evaluate the desired tensor and improve the denoising performance. In order to verify the effectiveness of proposed algorithm, both numerical simulation and experimental bearing failure data are analyzed. [ABSTRACT FROM AUTHOR]

The paper presents the results obtained during the years of vibration and current signature monitoring at Márkushegy mining plant, before its final closure. The large amount of recorded data has been used for a statistical analysis, being useful for deriving conclusions regarding the incidence of different kind of faults occurred at main underground and surface equipment of an underground coal mine. The importance of inter-inspection time period on the capability of monitoring to increase up-times is revealed. The gained experience, which is disseminated in the paper could be a valuable guideline to design the condition-based maintenance of operating mines. [ABSTRACT FROM AUTHOR]