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The transformation of the mining machinery industry is oriented towards the adoption of highly sophisticated technologies, with the aim of enhancing operational efficiency. Specifically, there is a focus on improving the nature and quality of operations carried out, such as vibrating screen. Vibrating screens, being one of the pivotal machines, have gained extensive utilization in mining processing plants. However, the demanding working conditions can lead to potential failures in certain components of vibrating screens. Bearings are one of the components that are susceptible to faults. Moreover, owing to the unique working environment and structure of vibrating screens, the signals extracted from defective bearings in vibrating screens exhibit greater complexity and distinctiveness, including (i) vibration interference related to the unbalanced shafts, (ii) cyclic impulses produced by the damaged bearing, (iii) non-cyclic random impulsive noise owing to pieces of coal falling into the deck, and (iv) inevitable noise from environmental conditions. To address this issue, an alternative procedure for bearing fault detection from vibrating screens served in the coal industry is proposed in the work. The strategy for bearing fault signature extraction covers three main steps: (a) a new fast resampled iterative filtering (FRIF) method is utilized to remove the vibration interference produced by the unbalanced shafts and simultaneously eliminate large amounts of background noise; (b) a signal processing method based stable distribution is designed to cancel the random impulses (impulsive noise); and (c) an improved energy operator demodulation technique is proposed to identify the bearing fault characteristics from the purified signal. The proposed strategy is validated using simulated and real tests. [ABSTRACT FROM AUTHOR]

• A dynamic model of bolted joint rotor system considering rubbing and pedestal looseness fault is established. • The effect of the rubbing fault on dynamic properties of the bolted joint was evaluated. • The effect of the rubbing-pedestal-looseness coupled fault on dynamic properties of the bolted joint was studied. • Experimental studies is conducted on a bolted joint rotor-bearing test rig with pedestal and rubbing device. Rotor-stator rub and pedestal looseness are common faults in rotating machinery, such as large gas turbines that typically use bolted joint structures to fasten adjacent disks together. It is of great significance to investigate the vibration behaviors of the bolted joint rotor-bearing system with pedestal looseness and rubbing coupled fault, which can provide proofs for the fault diagnosis. In the present work, a dynamic model of the bolted joint rotor-bearing system with coupled fault is developed by using the finite element (FE) method, and then the vibration characteristic analysis is carried out. The rotor shafts that connected by the bolted joint element (BJE) are discretized as two sets of Timoshenko beam elements. The BJE is composed with four springs (two linear translational springs and two rotational springs with piecewise-linear stiffness). The interactions of rotor-stator contact are then modeled based on the Hertz contact theory and Coulomb's friction law, and the bearing forces are also calculated based on the Hertz contact theory. Moreover, the pedestal looseness fault is modeled through the piecewise-linear stiffness spring. The equations of motions of the bolted joint rotor-bearing system with coupled fault are then developed through FE theory and solved by the Newmark method. The dynamic responses are then calculated, and the frequency spectrums, bending stiffness of bolted joint, and vibration responses under different rotation speeds under the coupled fault are analyzed. The experimental study is finally conducted based on a bolted joint rotor test rig. Simulation results are in agreement with experiment results. The research results can provide a basis for understanding the rotor dynamic features of a bolted joint rotor system with coupled faults of rubbing and pedestal looseness. [ABSTRACT FROM AUTHOR]

This work is focused on performance computation of high speed rotor bearing system with partial composite texture. Since its viscosity at varying speeds affects working performance in different partial composite texture, therefore at design and development stage, it is necessary to know the composite texture and thermal effect acting on rotor‐bearing system that causes variation of performance. The effects of partial composite texture size and position on the performance of the bearing are studied. After the optimal structural parameters are determined, the effects of the partial composite texture and fluid thermos structure coupling on the bearing capacity, stiffness, and friction coefficient of the oil film are analysed. The results show that after considering the influence of thermal effect, the performance enhancement of composite texture bearing is better than that of smooth bearing. Considering the temperature effect, the bearing capacity of the composite textured bearing is increased by 51.4% compared with that of the smooth bearing, and the friction coefficient is reduced to 22.4%, which is better than the value without considering the temperature effect, the accuracy of the results is verified by experiments. This study provides a theoretical basis for the design of hydrostatic bearing and improving its performance. [ABSTRACT FROM AUTHOR]

There is still a lack of effective lubrication condition monitoring methods in the field of diesel engines. The paper proposes a novel thermoelectric approach to divide the lubrication state of bearings. First, the generation mechanism of thermoelectric potential on bearings is clarified. Then, both experimental and simulation studies are done, and a strong correlation between lubrication and thermoelectric potential is shown. The film thickness and temperature are further confirmed as significant factors influencing thermoelectric potential. Generally, the thermoelectric potential increases with temperature. However, a small film thickness ratio (when the film thickness ratio is less than 4) will suppress the thermoelectric potential. Three typical lubrication states of bearings are distinguished through thermoelectric potential and supported by the Stribeck curve results. Moreover, the significant influence of lubrication on the bearing is confirmed through the analysis of surface morphology and composition. [ABSTRACT FROM AUTHOR]

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]

This paper presents a comprehensive methodology for developing a vibration measurement system, with a primary focus on monitoring rolling element bearings, widely recognised as pivotal components in generic machine vibration monitoring. Through the utilisation of sensors to capture vibration data signals, the system aims to meticulously analyse these signals to discern any indications of bearing health deterioration, thus facilitating the early identification of potential pre-breakdown conditions. The proposed system initiates the process by internally decomposing the three-axis vibration signal, enabling subsequent transmission of processed data to a remote location for the identification of specific faults in the roller element bearings. Installation of a three-axis accelerometer on the bearing housing, positioned radially, facilitates the capture of raw vibration data along one axial and two radial directions. Moreover, efforts to enhance computational efficiency involve cascading multiple identical modules in a serial pipeline structure, allowing for iterative loop computation of intrinsic mode functions within Empirical Mode Decomposition (EMD). Real-time computation is achieved through this serial pipeline structure, necessitating two distinct clock frequencies: one at 12.5 MHz for sampling and storing 3-axis vibration data in ping pong buffers, and another at 40 MHz for reading and processing the data signal retrieved from these buffers. Validation of the proposed method is demonstrated through simulation results obtained via synthesised Verilog code on Xilinx Artix-7 FPGA (XC7A35T-1CPG236C), affirming its efficacy in real-time applications. [ABSTRACT FROM AUTHOR]

In this paper it is presented a reconstruction of the bearing of the vortex turning unit on the LITOSTROJ PD 2000–6000 SM lathe, located in the maintenance plant of the hydro power plant Đerdap 1, which is used for processing the turbine blade sleeve as well as for processing other large machine parts. A newly designed bearing that transmits both types of loads (radial and axial) is shown through research process and finding a new solution. The bearing thus defined is monitored during exploitation by algorithmic software program C4.5 techniques with the aim of monitoring the operation of the new bearing solution, as well as estimating the remaining service life of the bearings. [ABSTRACT FROM AUTHOR]

The bearing group is a key load-bearing structure of positive displacement motor (PDM), which experiences significant alternating heavy loads during the drilling process and causes failure accidents. In order to study the failure mechanism of the bearing group, the bearing group of the 165mm positive displacement motor was taken as the research object. Based on the structural characteristics of the drive shaft assembly and the point contact theory of the thrust ball bearing, the load of the thrust bearing was analyzed and calculated. A finite element model was established for the nonlinear contact problem between the steel ball, stator, and rotor, and numerical analysis was conducted for ball bearings with different degrees of wear. The research results indicate that: 1) it is easy to cause fatigue fracture of the inner and outer rings when the series bearing operates for a long time and under the special working conditions. In addition, the inner ring of the string bearing has problems such as unqualified surface roughness and local hardness, which exacerbates the damage of the string bearing. 2) As the wear increases, the contact area between the bearing stator, rotor, and ball gradually decreases, and the stress concentration at the contact area between the steel ball and the stator and rotor intensifies, resulting in a significant increase in contact stress. 3) The maximum Mises stress, contact force, and contact stress that bearings are subjected to are greatly affected by ball wear. Once the ball wears, the stress concentration phenomenon will lead to a rapid increase in the bearing capacity of the shaft, which will intensify with the increase of drilling pressure. 4) Under the same working conditions, the maximum stress of the ball after wear of 1.5mm increased by 7.3 times compared to that without wear, resulting in bearing failure. The research methods and results of this article provide theoretical guidance for reducing the failure accidents of positive displacement motor bearing groups. [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]

In this study, we applied 1D convolutional neural networks (1D-CNNs) to permanent magnet synchronous motor (PMSM) fault diagnosis on 12 common PMSM fault types, namely, normal motor (Class A), poor dynamic balance of rotor (Class B), bent shaft (Class C), magnet demagnetization (Class D), uneven air gap (Class E), rotor misalignment (Class F), stator coil three-phase imbalance (Class G), stator coil layer short circuit (Class H), poor lubrication of bearing (Class I), damaged inner ring of bearing (Class J), damaged bearing ball (Class K), and poor assembly (Class L). First, a vibration spectrum analyzer was used to measure and capture the vibration signals of a faulty motor. Then, the 1D-CNN was utilized to analyze and diagnose the captured data. The results showed that the proposed 1D-CNN method can identify 11 motor fault types with an accuracy of up to 99.7%, higher than the 96.1% accuracy of 2D convolutional neural networks (2D-CNNs). In addition, the fault diagnosis system developed in this study can perform a rapid motor fault diagnosis with a small amount of training data, significantly reducing the detection cost for PMSM fault diagnosis. [ABSTRACT FROM AUTHOR]

Steel bearings have been commonly used to counteract induced loading from thermal and traffic conditions in numerous bridges. However, their effectiveness has been compromised due to aging and maintenance limitations, potentially impacting the overall bridge system performance. Existing monitoring techniques for detecting malfunctioning steel bearings lack automation and precision, making them inadequate for long-term and real-time bridge dynamics assessment. This study proposes a response-based approach to identify bearing malfunction by analyzing the traffic-induced response in the bearing vicinity. To implement this approach, laser displacement sensors and wireless acceleration sensors were employed to monitor both malfunctioning and well-functioning steel bridge bearings. Significant differences in bearing performance were observed through response analysis and comparison. Laser sensor measurements revealed larger vertical deflections in the girder at malfunctioned bearing under traffic loading. Moreover, the investigation of the acceleration response in the bearing locality indicated that bearing malfunction could alter the vibrational characteristics of the vicinity, significantly affecting Cross Power Spectral Density (CPSD) and cross-correlation. To quantitatively evaluate the performance of steel bearings, a Condition Score (CS) was introduced. The CS exhibited a strong correlation with bearing damage, providing valuable insights for maintenance and decision-making processes in bridge asset management. This study offers a comprehensive and automated method for identifying steel bridge bearing malfunction by utilizing advanced monitoring techniques and introducing the CS for assessment. The results obtained from this approach can enhance bridge maintenance strategies and contribute to effective bridge asset management. [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]

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]

Local failures (loss of concrete or reinforcement) can severely compromise the bearing capacity of shield segments, damaging the tunnel structures. To investigate the effects of local openings on the bearing behavior and failure mechanism, four full-scale bending tests were conducted on specimens with different opening positions and diameters; monitoring of load, displacement, and concrete strain was performed during loading. The test results reveal that both the opening position and diameter significantly influence the bearing characteristics of the segment. The failure process includes four sequential stages distinguished by three critical loads, namely the cracking, failure, and ultimate loads. Subsequently, the numerical model of the local failure segment was established using the elastoplastic damage constitutive relation of the concrete and verified by inversing the full-scale test results. Based on the numerical model, parametric analyses were performed to comprehensively investigate the influences of the opening position, concrete loss, and reinforcement loss on the bending capacity. Furthermore, an analytical model was proposed, indicating that the opening position is the primary factor decreasing the bearing capacity, followed by the opening diameter and reinforcement loss. The results of this study can provide a theoretical basis for the safety assessment and remedial design of subway shield tunnels under extreme breakthrough conditions. [ABSTRACT FROM AUTHOR]

In order to reduce the vibration of bearings in the bearing rotor system, a set of adaptive preload device based on pneumatic control was proposed. The vibration suppression effect of preload on the rotor and bearing roller at different rotating speed was studied based on preload force and rotating speed. An active control system composed of data acquisition system and pneumatic system was developed to monitor the vibration state of the bearing in the process of rotation in real time, and the preload was adjusted timely to ensure that the vibration was suppressed to the greatest extent during the normal operation of the bearing. The rotor system equipped with an aligning roller bearing (22208CA) was tested to verify the function of the device. The experimental results show that the vibration suppression effect of the device is more than 30% when the rotor speed is less than the first critical speed, and the bearing vibration amplitude is greatly reduced. Compared with the existing intelligent bearing units, the device provides a more efficient and simple solution for vibration suppression of the rotor bearing system. [ABSTRACT FROM AUTHOR]

The presence of strong noise and vibration interference in fault vibration signals poses challenges for extracting fault features from motor bearings. Therefore, appropriate pre-filtering procedures can effectively suppress the impact of the noise interference and further enhance fault-related signals. In this work, an improved Laplacian-of-Gaussian (ILoG) filter is proposed to enhance the fault-related signal. The proposed ILoG approach employs an enhanced Kurtosis-based indicator known as Correlated Kurtosis (CK). The CK capitalizes on the cyclostationarity of fault-related impulses and mitigates the random nature of impulse noise. Subsequently, an objective function, based on CK statistics, is suggested to iteratively update LoG coefficients by maximizing the CK value of the output signal. Therefore, the ILoG filter can better highlight the fault cyclic impulses associated with bearing faults. Furthermore, the ILoG filter is capable of attenuating impulsive noise, a feature that is absent in the original LoG filter. The simulation and experimental results demonstrate that the proposed ILoG method provides a remarkable capability to effectively enhance the fault-induced components, thereby improving the diagnostic accuracy. Consequently, the ILoG filter holds great potential for application in motor bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

To fully evaluate the influence of the rough surface (𝜎) and speed (𝜔) of the crankpin bearing on the engine power, a combination model of the slider crank mechanism and crankpin bearing's lubrication is established to calculate the mathematical equations for the simulation. Three indexes of the bearing-capacity (𝑊), friction-force (𝐹), and friction-coeficient (𝜇) are used to evaluate the influence of the change of the crankpin bearing's speed and rough surface on the engine's power. The study shows that increasing 𝜔 not only effectively reduces the load capacity of the crankpin bearing but also increases the 𝐹 and 𝜇 in the engine's crankpin bearing, thereby directly reducing the engine's power. Besides, the reduction of 𝜔 also reduces the bearing-capacity of the crankpin bearing. To optimize the engine's power, the engine's speed should be maintained at 2000 r/min to improve the engine's power. In addition, under the effect of the rough surface of the crankpin bearing, the 𝑊 of the crankpin bearing is insignificantly affected by the change of the rough surface while both the 𝐹 and 𝜇 are greatly affected. In particular, the maximum 𝐹 at 𝜎 = 8 μm and 𝜎 = 10 μm is increased by 68.3 % and 77.7 % in comparison with the maximum 𝐹 at minimum value of 𝜎 = 2 μm, respectively. Therefore, in the design of the engine, the rough surface of the crankpin bearing should be reduced to improve the engine's power. Additionally, the design parameters of the crankpin bearings should also be optimized to further improve the engine's power. [ABSTRACT FROM AUTHOR]

Lead rubber bearings (LRBs) are a type of isolation bearing that have a combination of rubber and lead as the main components. These bearings are widely used in bridges, buildings, and other important structures due to their high load‐carrying capacity and excellent energy dissipation capability. However, the behavior of LRBs is complex and nonlinear, making it difficult to predict their behavior and performance under different loading conditions. The objective of this research is to develop a comprehensive analytical model of LRBs that can accurately predict their behavior under low to large levels of strain. The proposed model considers nonlinearity, hysteresis, stiffness, damping, and rate‐dependent behavior of LRBs. The model is also able to consider the effect of temperature on the rubber and lead components of the bearing. The developed model is validated using experimental results and is shown to provide accurate predictions of the LRB response under different strain levels. The accuracy of the developed LRB model is also validated using shake table test results of an LRB‐isolated bridge under low and large strain. This research provides a valuable tool for engineers and designers to predict the behavior and performance of LRBs and optimize their design. [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]

Aiming at the nonlinear vibration problem caused by the dynamic misalignment of rolling bearings in the rotating machinery, a rotor dynamic model with bearing dynamic misalignment is established, and the effects of the dynamic misalignment of single and two bearings and the phase relationship of two dynamic misalignment bearings on the rotor vibration characteristics are researched. The effects of bearing clearance, raceway curvature radius, the ball number and radial load on the nonlinear vibration response of the dynamic misalignment bearing-unbalance rotor system are further analyzed. The results show that the dynamic misalignment of the bearing can excite the vibration at twice the rotor frequency, raise the resonance speed and enhance the hardening-type nonlinearity characteristics of the rotor. The influence of dynamic misalignment under two bearings with out-of-phase on rotor resonance is greater than that of the in-phase. The stability of the system can be improved by decreasing the bearing clearance and increasing the curvature radius coefficient. The vibration caused by bearing dynamic misalignment can be reduced by selecting the bearing with more balls and appropriately increasing the bearing load. [ABSTRACT FROM AUTHOR]

When the surface roughness is comparable to the surface separation in a hydrodynamic thrust bearing, the effect of the surface roughness should be considered. In the condition of low bearing clearances such as on the scales of 1 nm and 10 nm, normally not only the surface roughness but also the physically adsorbed layer on the bearing surface should be simultaneously considered in evaluating the bearing performance. The present paper presents the numerical calculation results of the surface roughness influences on the hydrodynamic pressure and carried load of the inclined fixed pad thrust bearing with low bearing clearances when the effect of the adsorbed layer is incorporated. It is shown that the influence of the surface roughness is strongly dependent on the adsorbed layer and it is significantly increased with the increase in the interaction strength between the fluid and the bearing surface when the bearing clearance is low. For a weak fluid-bearing surface interaction, the results are close to those obtained from the classical hydrodynamic theory indicating the increase in the hydrodynamic pressure and carried load of the bearing with the increase in the surface roughness, while for the medium or strong fluid-bearing surface interactions, this surface roughness effect is much stronger. The results reveal the new mechanism in the studied model of the bearing regarding the coupled effects of the surface roughness and the physically adsorbed layer on the bearing surface. [ABSTRACT FROM AUTHOR]

A monitoring system for induction motors (IMs) is essential for most industrial plants. Bearing faults and shaft misalignment are common mechanical defects in induction motors. Since one fault could cause another fault in the system, multiple faults can occur simultaneously and change the vibration (electrical) behaviour of the induction motors from that of a single fault condition. This paper aims to identify two common faults (shaft misalignment and defective bearing) simultaneously in IMs using data fusion of vibration and current measurements. Sensor fusion of accelerometer and Hall-effect sensor signals is used to combine the vibration and current signals. The proposed method is applied via a laboratory test-rig based on data fusion to detect multiple defects simultaneously in induction motors. Then, by extracting the important features using a principal component analysis (PCA) algorithm, the K-nearest neighbours (KNN) classification algorithm is used to detect defects and make decisions. The results show that the fusion of both current and vibration signal analyses significantly improves the efficiency and reliability of multiple fault detection. Also, bispectrum analysis of the current signal is highly sensitive to misalignment and can be an effective method for detecting such faults. [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]

Rotating machines are an important part of industrial equipment. It is essential to improve their performance while reducing the manufacturing, operating, and maintenance costs. Ensuring their reliability is also crucial because a machine breakdown can result in significant costs and potential environmental and safety damage. Reliability-based optimization is an approach that aims to find an optimal and robust design that guarantees a machine's reliability. In this study, we focused on optimizing the shaft diameter and oil temperature of a rotor supported by hydrodynamic bearings. We considered the materials' elastic moduli, density, and bearing clearance as uncertain parameters. Our goal was to ensure 99% reliability regarding both the vibration amplitude and stability threshold. To model the machine, we used the finite element method and represented the bearings using stiffness and damping coefficients, considering the linear short bearing model. Due to the complexity of the model, we employed surrogate models to solve the reliability-based optimization problem. Our results showed that the optimization problem could be solved successfully using Kriging, polynomial chaos expansion, and polynomial chaos Kriging. [ABSTRACT FROM AUTHOR]

In contrast to the multitude of models in the literature for the calculation of rolling contact fatigue in rotating bearings, literature on oscillating bearings is sparse. This work summarizes the available literature on rolling contact fatigue in oscillating bearings. Publications which present various theoretical models are summarized and discussed. A number of errors and misunderstandings are highlighted, information gaps are filled, and common threads between publications are established. Recommendations are given for using the various models for any oscillating bearing in any industrial application. The applicability of these approaches to pitch and yaw bearings of wind turbines is discussed in detail. [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]

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]

In the field of electric aircraft, the axial force of the conical motor is used to offset propeller thrust and reduce bearing wear. For the problem of accurate calculation of the no‐load axial force of the conical motor, combined with the characteristics that the rotor magnetic bridge of the hybrid conical inner‐mounted permanent magnet synchronous motor (HC‐IPMSM) is easy to be saturated and difficult to be calculated analytically, an equivalent analytical calculation method is proposed. According to the principle of constant air‐gap magnetic flux, the HC‐IPMSM is equivalent to many cylindrical surface‐mounted permanent magnet synchronous motors (CY‐SPMSM), and an analytical model is gained. Combining with the scalar magnetic potential Laplace equation and Poisson equation, the air‐gap flux density distribution on the rotor surface is calculated. And finally, the no‐load axial force of the motor is obtained by using the cone angle and the Maxwell stress method. Simulation and experiments verify the correctness and validity of the analytical model. [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]

There are few studies on the fault diagnosis of deep learning in real large-scale bearings, such as wind turbine pitch bearings. We present a novel fault diagnosis method, Bayesian augmented temporal convolutional network (BATCN), to filter the raw signal in wind turbine pitch bearing defect detection. This method, which employs temporal convolutional neural networks, is designed to capture the temporal dependencies of the signal, with such a focus on non-stationary relationships in the collected signals. By referring to the thoughts of Bayesian optimization, our approach can spontaneously find the best patch length that influences fault signal extraction during the filtering process, avoiding manual tuning of this hyper-parameter. This BATCN method is first performed on simulation signals and an open-source dataset of general bearings, and then validated on industrial wind turbine pitch bearings both in the lab and in the real wind farm, where the bearings have been operated for over 15 years. The results show that our method can work well for large-scale slow-speed wind turbine pitch bearings. [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]

Deep domain adaptation techniques have recently been the subject of much research in machinery fault diagnosis. However, most of the work has been focused on domain alignment, aiming to learn cross-domain features by bridging the gap between source and target domains. Despite the success of these methods in achieving domain alignment, they often overlook the class discrepancy present in cross-domain scenarios. This can result in the misclassification of target domain samples that are located near cluster boundaries or far from their associated class centers. To tackle these challenges, a novel approach called deep domain adaptation with correlation alignment and supervised contrastive learning (DCASCL) is proposed, which synchronously realizes both domain distribution alignment and class distribution alignment. Specifically, the correlation alignment loss is used to enforce the model to generate transferable features, facilitating effective domain distribution alignment. Additionally, classifier discrepancy loss and supervised contrastive learning loss are integrated to carry out feature distribution alignment class-wisely. The supervised contrastive learning loss leverages class-specific information of source and target samples, which efficiently promotes the compactness of samples of the same class and the separation of samples from different classes. Moreover, our approach is extensively validated across three diverse datasets, demonstrating its effectiveness in diagnosing machinery faults across different domains. [ABSTRACT FROM AUTHOR]

Purpose: This work aims to introduce a rotating machine's defects localisation based on a time reversal method. We are interested in the study of ball bearing defects localisation by the time reversal method. Bearings are usually the most subjected components to wear in industrial rotating machines. Vibration analysis method is commonly used to investigate and extract knowledge about this type of faults as soon as they appear. This time, as a new solution, we introduced the time reversal method to this field. Methods: Generally, the time reversal method is performed in two steps; a propagation media characterization (also known as recording step) then a localisation or focusing (back propagation step). Bearing, gears and balancing defects are some of the main defects that might occur in rotating machines. Those defects act like exterior actions which excites the machine structure. The effect of these actions follows a precise path to propagate. Thus, the first objective in this study is to characterise experimentally that path between a random position on the structure and some areas that may contain a defect. Then, apply the time reversal process to locate defected components. It has been shown in the literature that complexity of the propagation media or structure improves the efficiency of the time reversal method. Wave reflexion/conversion due to the media characteristic could add information about the source location, which is equivalent to virtual sensors. In this study case, it is due to vibrations transfer between components by contact. We propose performing a characterization step when the machine is at rest because the effect of defects occurs only if the machine is rotating. As a first investigation in this field, the interest is made on bearing defects. Results: The number of sensors used in the analysis can be reduced, using the time reversal method to locate defects, to a single accelerometer. The time reversal process sometimes needed combining the measurements of multiple sensors to extract the defect location. The time reversal technique makes it possible to separate the sensors from the active components. Rather than using accelerometers in the bearings, as conventionally done, the method was able to locate the fault in the structure. Conclusion: The time reversal method has a potential to be combined with other techniques to locate accurately any vibration sources in rotating machines, particularly those produced by periodic shocks. [ABSTRACT FROM AUTHOR]

The present study was conducted to evaluate the analytical precision of finite element analysis models of wind turbine bearings. In the finite element analysis models, balls were modeled as finite element meshes as a solid model or replaced by nonlinear springs as two kinds of spring models. In addition, test bench modeling was performed to calculate the displacement following the application of a turnover moment by means of a global model analysis and to calculate the contact stress by means of a sub-model analysis. The comparison of the results of the finite element analyses with the results of the bearing bench test showed that the analytical precision was 17% in the single-spring model, 9% in the Daidie spring model, and 3% in the finite element mesh ball model, indicating that the finite element mesh ball model exhibited the highest precision. [ABSTRACT FROM AUTHOR]

Due to the recent advances in the development of smart rotating machineries, there is a high demand for automated support bearings with efficient integrated control systems. Existing research studies have indicated that high-end performance can be attained from automated bearings through effective control and modification of the bearing performance parameters. In this study, an innovative journal bearing geometry with multi-control operations is presented. The four controllable bearing pads enclosed in the bearing casing have a novel feature to translate radially and undergo controlled tilt motions. The multi-control bearing system with radial and tilt pad motions will significantly influence the stability responses of the rotorsystem, which is theoretically analysed in this study. To predict the variation in film thickness for varied pad adjustments, a modified film thickness equation is incorporated in the linearised perturbed model for dynamic analysis. A notable variation in dynamic coefficients and stability parameters are observed for negative radial adjustment and tilt angles. Results indicate that negatively adjusted radial and pad tilt motion can generate improved stability margins at higher eccentricities. Data generated on stability margins at specific pad adjustments will be helpful while developing the control system for the actuation mechanism in the experimental setup. [ABSTRACT FROM AUTHOR]

The main aim of this article is to achieve predictive maintenance by proposing reliable residuals specific to outer ring bearing faults. Our work falls within the general context of maintenance, with particular emphasis on vibration analysis techniques. For an in-depth study of the fault, we use spectral analysis of electromagnetic torque, which gives very satisfactory results compared with work based on line current or neutral voltage fault signatures due to the similarity in the mechanical nature of the fault and the signal to be studied. In fact, an analytical calculation of the electromagnetic torque has been developed to obtain the specific frequencies of the fault under consideration. To highlight our results, the simulation of the analytical calculation of this fault was implemented in MATLAB/SIMULINK using the Fast Fourier Transform method to extract fault signatures. The induction motor's performance was analyzed under various operating conditions, including both healthy and faulty states. Finally, an experimental study will support our analytical developments. [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]

In 2015, a 45 MW vertical hydropower machine exhibited excessive vibration after refurbishment. Measurements revealed a substantial bearing clearance at the lower generator guide bearing. Consequently, the bearing was unable to generate sufficient opposing force to drive the rotor toward the bearing center, resulting in more pronounced overall system vibration. Addressing this challenge required a cost-effective and feasible solution for mitigating the vibration problem. To this end, a design modification was implemented wherein the lower generator guide bearing (originally a sleeve bearing) was modified to a four-lobe bearing by offsetting the two halves of the bearing twice in two axes. Numerical simulations and experimentations were conducted, and the dynamics of the machine before and after the design modification were investigated. Both the simulation and experimental results showed that the machine with the four-lobe bearing improved the system stability and reduced the vibration amplitudes. The numerical simulation result demonstrated that, due to the design modification, the first and second critical speeds were effectively eliminated for a speed range of up to three times the nominal speed. Furthermore, for nominal operation with unbalanced magnetic pull, the four-lobe bearing provided a stability advantage in terms of the modal parameters relative to the original sleeve bearing. [ABSTRACT FROM AUTHOR]

Purpose: Thrust bearings play a critical role in high-speed rotating machinery, such as turbines, pumps, compressors, and turbogenerators, by transferring axial loads between the collar and the bearing pads. The lubricating fluid prevents contact, friction, and wear between solid parts and acts as a cooling medium. The purpose of this study is to evaluate the first- and second-order bearing coefficients of an inclined pad hydrodynamic thrust bearing, which have not been previously investigated, to improve the accuracy of modeling thrust bearings. Methods: In the analysis of thrust bearing systems, the lubricating fluid film is modeled as a massless spring-damper system. The finite perturbation method for the governing Reynolds equation is used to calculate the dynamic coefficients of the thrust bearing. This is done using the finite difference method (FDM) in polar coordinates. Results: This study investigates the influence of misalignment, rotating speed, and mesh size on the bearing coefficients of the thrust bearing. The results show that misalignment angle and film thickness have a clear effect on the dynamic coefficients, while changing the rotational speed has no effect on the damping coefficients. The study also investigates the axial force and moments and dynamic coefficients of an inclined pad thrust bearing. Conclusion: This study concludes that evaluating the first- and second-order bearing coefficients of the thrust bearing using the finite perturbation method can improve the accuracy of modeling thrust bearings. The study also highlights the significant influence of misalignment and film thickness on the dynamic coefficients of the thrust bearing. The results presented in this study provide valuable data that can be used as input for rotor dynamics analyses in high-speed rotating machinery. [ABSTRACT FROM AUTHOR]

Purpose: The most common cause of CNC machine failure is the bearing faults that influence the performance of the manufacturing system. The detection and the diagnosis of bearing defects are crucial to the reliable functioning of revolving machinery. This paper suggests an intelligent vibration-based condition and fault diagnostic technique for the identification of bearing faults in CNC machine. Investigational vibration data obtained for various bearings and operational requirements were analyzed to create a structure for the monitoring and classification of bearing defects to determine the health of the machine. Methods: Fault diagnosis was made using Hybrid Signal Decomposition (HSD) methodology for the decomposition of the vibration signal. Vibration features derived from the received decomposed raw signal were chosen using the key component review to eliminate redundant features using Principal Component Analysis (PCA). Subsequently, these related features were input into Discrete AdaBoost and Gentle AdaBoost and compared to traditional Machine Learning (ML) algorithms for the classification of various bearing faults in CNC machine tools. Results: The proposed Gentle AdaBoost is outperformed with 100% classification accuracy by Discrete AdaBoost and other machine learning algorithms. Experimental outcomes suggest that the proposed approach has an immense capacity to prevent unplanned and unnecessary device shutdowns due to loss of bearings in the CNC machine. Conclusion: The success rate achieved using Gentle AdaBoost outperformed Discrete AdaBoost and other traditional machine learning algorithms, the performance rate obtained for defining various bearing states dependent on vibration signals. [ABSTRACT FROM AUTHOR]