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Accurate and timely bearing fault detection is imperative for optimal system functioning and the implementation of preventative maintenance measures. Deep learning models provide viable solutions to these malfunctions, however, the lack of labelled data makes the training both expensive and cumbersome. To remedy this, various semi-supervised approaches have surfaced in the last decade, significantly mitigating the need for extensive labelled data but with added computational cost. This study proposes one such approach by leveraging generative adversarial networks (GAN) trained on a time-frequency based representation. The proposed Parallel Convolutions Semi-Supervised GAN, namely PC-SSGAN, uses bottleneck parallel convolutions blocks to capture multi-scale features in both local and global contexts, lacing both the generator and discriminator with enhanced feature extraction capabilities and simultaneously reducing the parameters and training time. The Proposed framework is evaluated on two distinct open-source datasets. The classification accuracy for both models exceeded 99.50%. Moreover, the proposed parallel convolutions-based architecture spent approximately 33% less time on training than the normal convolutional layers. It has been foreseen that the proposed fault detection system can be integrated into the motor fault tolerant control system to produce a unified framework that can make informed decisions to handle the bearing faults effectively. [ABSTRACT FROM AUTHOR]

Steel racking systems are widely adopted for storage purposes: they are thin-walled structures composed of consecutive trusses, connected with beams on which the palletized goods are stored. Their geometry and structural configuration strongly depend on market and operator necessities, and, in modern applications, racks can also function as the supporting structure of the warehouse itself in the form of Rack Supported or High-Bay Warehouses. With the increase of the overall geometric dimensions and the global weight of the stored material, the seismic action becomes more relevant for the design. Along these lines, the development and experimental testing of a dedicated seismic design approach for ductile steel racks is here presented, with particular attention to Rack Supported Warehouses. This approach exploits the ductility of trusses introduced via the plastic ovalization mechanism of the diagonal-to-upright connections while a tailored capacity design is used to assure the elastic behaviour of the rest of the structure and to keep the brittle failure mechanisms at bay. [ABSTRACT FROM AUTHOR]

Abstract A 3-dimensional model of the angular contact ball bearing (ACBB) was modeled using Abaqus/standard (Dassault systems- version 2017) to investigate the influence of race thickness on the bearing performance. It was found that the ability to support higher contact stress increased with race thickness. However, large deformations were found to occur on outer race with thickness of 3.3 mm and only small deformations were observed on outer race with a thickness of 9.9 mm. The large deformations induce higher shear stresses on thin races than on thick races. These stresses cause spall growth in bearings and propagate into a network of cracks. As a result of these findings, thin races are prone to failure compared with thick races.

A 3-dimensional model of the angular contact ball bearing (ACBB) was modeled using Abaqus/standard (Dassault systems- version 2017) to investigate the influence of race thickness on the bearing performance. It was found that the ability to support higher contact stress increased with race thickness. However, large deformations were found to occur on outer race with thickness of 3.3 mm and only small deformations were observed on outer race with a thickness of 9.9 mm. The large deformations induce higher shear stresses on thin races than on thick races. These stresses cause spall growth in bearings and propagate into a network of cracks. As a result of these findings, thin races are prone to failure compared with thick races. [ABSTRACT FROM AUTHOR]

Due to their lightness and simple connectivity, steel racking systems are typically considered as "low-dissipative" structures, which is reflected in the modern seismic codes by the absence of capacity design and the adoption of low behaviour factors. This limited capability of stress redistribution significantly increases the vulnerability of racks under beyond-design seismic hazards and raises the demand for more resilient designs. Along these lines, the proposed Plastic Ovalization Strategy (POS) attempts to increase the ductility of the individual upright frames comprising the cross-aisle direction of racks, and at the same time to preserve their low-cost and easy-to-assemble nature. This is achieved by tasking the bearing failure mechanism of the diagonal bolt hole to absorb seismic deformations, while capacity design is employed to keep the rest of the structure in the elastic zone. Following a detailed discussion on the motives and basic principles of the strategy, two high-rise racking systems are designed twice by professional engineers, once using standard approaches and then by additionally employing the proposed POS rules. Finally, the two design solutions are compared by conducting a comprehensive seismic assessment, which employs a phenomenological macro-model comprising elastic elements and nonlinear springs to simulate the bearing failure mechanism. [ABSTRACT FROM AUTHOR]

Roller bearings are critical components in various mechanical systems, and the timely detection of potential failures is essential for preventing costly downtimes and avoiding substantial machinery breakdown. This research focuses on finding and verifying a robust method that can detect failures early, without creating false positive failure states. Therefore, this paper introduces a novel algorithm for the early detection of roller bearing failures, particularly tailored to high-precision bearings and automotive test bed systems. The featured method (AFI—Advanced Failure Indicator) utilizes the Fast Fourier Transform (FFT) of wideband accelerometers to calculate the spectral content of vibration signals emitted by roller bearings. By calculating the frequency bands and tracking the movement of these bands within the spectra, the method provides an indicator of the machinery's health, mainly focusing on the early stages of bearing failure. The calculated channel can be used as a trend indicator, enabling the method to identify subtle deviations associated with impending failures. The AFI algorithm incorporates a non-static limit through moving average calculations and volatility analysis methods to determine critical changes in the signal. This thresholding mechanism ensures the algorithm's responsiveness to variations in operating conditions and environmental factors, contributing to its robustness in diverse industrial settings. Further refinement was achieved through an outlier detection filter, which reduces false positives and enhances the algorithm's accuracy in identifying genuine deviations from the normal operational state. To benchmark the developed algorithm, it was compared with three industry-standard algorithms: VRMS calculations per ISO 10813-3, Mean Absolute Value of Extremums (MAVE), and Envelope Frequency Band (EFB). This comparative analysis aimed to evaluate the efficacy of the novel algorithm against the established methods in the field, providing valuable insights into its potential advantages and limitations. In summary, this paper presents an innovative algorithm for the early detection of roller bearing failures, leveraging FFT-based spectral analysis, trend monitoring, adaptive thresholding, and outlier detection. Its ability to confirm the first failure state underscores the algorithm's effectiveness. [ABSTRACT FROM AUTHOR]

Roller mills are commonly used in the production of mining derivatives, since one of their purposes is to reduce raw materials to very small sizes and to combine them. This research evaluates the mechanical condition of a mill containing four rollers, focusing on the largest cylindrical roller bearings as the main component that causes equipment failure. The objective of this work is to make a prognosis of when the overall vibrations would reach the maximum level allowed (2.5 IPS pk), thus enabling planned replacements, and achieving the maximum possible useful life in operation, without incurring unscheduled corrective maintenance and unexpected plant shutdown. Wireless sensors were used to capture vibration data and the ARIMA (Auto-Regressive Integrated Moving Average) and Holt–Winters methods were applied to forecast vibration behavior in the short term. Finally, the results demonstrate that the Holt–Winters model outperforms the ARIMA model in precision, allowing a 3-month prognosis without exceeding the established vibration limit. [ABSTRACT FROM AUTHOR]

Bearings play a crucial role in vehicle structures and systems, enabling relative motion between essential components such as shafts and housings. However, during service, many bearings are prone to failure due to factors like excessive loading, improper lubrication and ineffective sealing. To optimize production and minimize maintenance costs, accurately forecasting a bearing's Remaining Usable Life (RUL) becomes essential. This study focuses on three different deep groove ball bearings used in diverse vehicle applications, including washing machines, electric motors, gear drives and pumps. Utilizing Python programming language and input parameters such as radial and axial loads, speed and shift hours, RUL of the bearings was calculated. The results demonstrated a strong correlation between RUL and shift hours as well as the load acting on the bearings. Notably, a 25% increase in bearing RUL was observed with a decrease in shift hours, facilitating informed decisions on optimal machine operating hours and cost-effective maintenance strategies for vehicle systems. [ABSTRACT FROM AUTHOR]

By experimentation, this study investigated interference-fit effects on the bearing failure of thin-ply composite countersunk single-lap joints. A comparative study of the mechanical behaviour and strengthening of the bolt-hole wall during the assembly of thin-ply and thick-ply specimens with varying interference sizes has been carried out. The effect of the interference-fit on the strength of countersunk composite bolted joints with thin-ply has been studied in detail under quasi-static tensile loading. The interface strengthening and failure mechanisms of thin-ply interference joints were characterized by means of SEM micrographs. Thin-ply laminates exhibited lower installation forces during assembly. The use of thin-ply in the interference joints' area could still demonstrate suppression of the damage's generation and propagation. More importantly, thin-ply laminates could accommodate larger interference sizes than thick-ply laminates and have higher load-bearing capacities, approximately 10%–20 % higher. Thin prepregs could be used to facilitate the design process and improve composites' structural properties, providing new perspectives for improvements and innovative applications of interference joining technology. The results of this study have some guidance for the aerospace application of thin prepregs in thin-walled structures.

The use of stainless steel in structures has been improved; nevertheless, the application of this material is not considered trivial by most designers, as there as still limited design codes providing specific rules. Current codes provide conservative approaches in certain cases, which do not corroborate with the objective of reducing the initial cost of projects incorporating stainless steel. In order to contribute to an adequate comprehension about the structural behavior of austenitic steel in bolted connections, a study was conducted regarding the influence of geometric parameters on the connections with a bolt under two shear planes, encompassing the evaluation of the phenomenon recognized as curling. Concerning to conclusions, it was observed that the geometric parameters e1, e2, d0, affects the curling effects, which has the potential to compromise the structural behavior of a thin‐plate connection if this deformation is greater than the thickness of the plate. Furthermore, the results provided by the equations employed in the design codes was assessed in this study, revealing an adequate prediction of the SCI in terms of the failure mode. However, both the manual and current codes exhibited conservative load for the investigated connections. [ABSTRACT FROM AUTHOR]

Research aimed at diagnosing rolling bearing faults is of great significance to the health management of equipment. In order to solve the problem that rolling bearings are faced with variable operating conditions and the fault features collected are single in actual operation, a new lightweight deep convolution neural network model called FC-CLDCNN, composed of a convolution pooling dropout group with two-stream feature fusion and cross-load adaptive characteristics, is proposed for rolling bearing fault diagnosis. First, the original vibration signal is transformed into a one-dimensional frequency domain signal and a two-dimensional time-frequency graph by fast Fourier transform and continuous wavelet transform. Then, the one-dimensional frequency domain signal and two-dimensional time-frequency diagram are input into the two channels of the model respectively to extract and recognize the one-dimensional and two-dimensional features. Finally, the one-dimensional and two-dimensional features are combined in the fusion layer, and the fault types are classified in the softmax layer. FC-CLDCNN has the characteristics of two-stream feature fusion, which can give full consideration to the characteristics of rolling bearing fault data, so as to achieve efficient and accurate identification. The Case Western Reserve University (CWRU) dataset is used for training and testing, and it is proved that the proposed model has high classification accuracy and excellent adaptability across loads. The Machinery Failure Prevention Technology (MFPT) dataset was used to validate the excellent diagnostic performance and generalization of the proposed model. [ABSTRACT FROM AUTHOR]

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Roller bearings are critical components in various mechanical systems, and the timely detection of potential failures is essential for preventing costly downtimes and avoiding substantial machinery breakdown. This research focuses on finding and verifying a robust method that can detect failures early, without creating false positive failure states. Therefore, this paper introduces a novel algorithm for the early detection of roller bearing failures, particularly tailored to high-precision bearings and automotive test bed systems. The featured method (AFI—Advanced Failure Indicator) utilizes the Fast Fourier Transform (FFT) of wideband accelerometers to calculate the spectral content of vibration signals emitted by roller bearings. By calculating the frequency bands and tracking the movement of these bands within the spectra, the method provides an indicator of the machinery’s health, mainly focusing on the early stages of bearing failure. The calculated channel can be used as a trend indicator, enabling the method to identify subtle deviations associated with impending failures. The AFI algorithm incorporates a non-static limit through moving average calculations and volatility analysis methods to determine critical changes in the signal. This thresholding mechanism ensures the algorithm’s responsiveness to variations in operating conditions and environmental factors, contributing to its robustness in diverse industrial settings. Further refinement was achieved through an outlier detection filter, which reduces false positives and enhances the algorithm’s accuracy in identifying genuine deviations from the normal operational state. To benchmark the developed algorithm, it was compared with three industry-standard algorithms: VRMS calculations per ISO 10813-3, Mean Absolute Value of Extremums (MAVE), and Envelope Frequency Band (EFB). This comparative analysis aimed to evaluate the efficacy of the novel algorithm against the established methods in the field, providing valuable insights into its potential advantages and limitations. In summary, this paper presents an innovative algorithm for the early detection of roller bearing failures, leveraging FFT-based spectral analysis, trend monitoring, adaptive thresholding, and outlier detection. Its ability to confirm the first failure state underscores the algorithm’s effectiveness.

The fatigue crack initiation direction of wind turbine gearbox bearings is investigated. Through theoretical research，it is believed that the direction of crack initiation depends on the direction of the maximum shear stress amplitude plane at the local initiation position. After calculation，it is found that the orthogonal shear stress amplitude of the shallow layer inside the bearing raceway is greater than the principal shear stress amplitude，and the principal shear stress amplitude at the deeper layer is greater than the orthogonal shear stress amplitude. Therefore，the crack initiation angle varies with the initiation depth. Aiming at the problem of crack propagation，the method of stress superposition in the polar coordinate of the crack tip in fracture mechanics is used to clarify the influence of Hertz contact stress，residual stress，assembly interference pressure，etc. on the direction of crack propagation. The applicability of the theory is verified by analyzing the failure cases of bearings with different thermal treatment microstructure.

Bearing fault is a factor that directly affects the reliability of the machine tools. Small sample bearing fault diagnosis plays an important role to improve the reliability of machine tools. However, the over-fitting and weak performance are common problems of small sample bearing fault diagnoses based on deep learning. This paper proposed a different method based on data enhancement and convolutional neural networks (CNN). The method firstly decomposes the vibration signals of the rolling bearing according to the optimal decomposition criterion of variational mode decomposition (VMD). Then, it selects the modes according to the fault frequency characteristics and filters the selected modes into multiple sub-band signals by band-pass filters. Moreover, it computes out the autocorrelation peak vector of the sub-band signals. Finally, the method uses the fault diagnosis network made from a 4-layer neural network, automatically extracts bearing fault features, and predicts the fault types of the testing signals. The experiment shows that the proposed method has a 99% accuracy rate in the rolling bearing fault data set XJTU-SY and requires fewer training samples than the latest methods of NKH-KELM and VMD-CNN. The proposed method has high accuracy under the small sample conditions, which makes it applicable in some practical CNC machine tools with difficulties obtaining bearing samples. [ABSTRACT FROM AUTHOR]

Roller mills are commonly used in the production of mining derivatives, since one of their purposes is to reduce raw materials to very small sizes and to combine them. This research evaluates the mechanical condition of a mill containing four rollers, focusing on the largest cylindrical roller bearings as the main component that causes equipment failure. The objective of this work is to make a prognosis of when the overall vibrations would reach the maximum level allowed (2.5 IPS pk), thus enabling planned replacements, and achieving the maximum possible useful life in operation, without incurring unscheduled corrective maintenance and unexpected plant shutdown. Wireless sensors were used to capture vibration data and the ARIMA (Auto-Regressive Integrated Moving Average) and Holt–Winters methods were applied to forecast vibration behavior in the short term. Finally, the results demonstrate that the Holt–Winters model outperforms the ARIMA model in precision, allowing a 3-month prognosis without exceeding the established vibration limit.

The envelope shape of a failure-induced impulse response reflects the strike procedure of the bearing failure area and provides information on the size and contour of the defective area. Because the impulse envelope is a broad-bandwidth component severely affected by in-band noise, it is difficult to separate from the vibration signals. To eliminate the in-band noise and obtain diagnostic information from the full-band envelope, a novel method, called variational multi-harmonic mode extraction (VMHME), is proposed to extract the impulse envelope component and characterise the envelope shape. First, a multi-harmonic mode function (MHMF) is constructed to define a harmonic assembly, which is generally has a broad-bandwidth property, but each harmonic is narrow-bandwidth. A variational model is then established to optimally decompose the mode with explicit MHMF from the analysed signal. Only one specific mode is extracted at a time and adopted to efficiently satisfy the demand for failure-induced impulse envelope extraction. Essentially, VMHME provides an optimal band-pass filter bank just with a local narrow bandwidth at failure characteristic harmonics. Utilising​ the narrow-bandwidth harmonic assembly eliminates the in-band noise, whereas the failure characteristics in the envelope shape are retained The effectiveness of the proposed method was verified using both simulated and experimental bearing failure signals. The results prove that VMHME can make in-depth use of the envelope shape information for bearing failure diagnosis. • The shape characteristics of an impulse envelope instead of the repetition periods is investigated. • A multi-harmonic mode function (MHMF) is constructed to define the shape characteristics of the impulse envelope. • A variational multi-harmonic mode extraction method was proposed to characterise the impulse envelope. • The shape information of impulse envelope is extracted and used for bearing failure diagnosis. [ABSTRACT FROM AUTHOR]

Bearings are crucial components that decide whether or not a wind turbine can work smoothly and that have a significant impact on the transmission efficiency and stability of the entire wind turbine's life. However, wind power equipment operates in complex environments and under complex working conditions over long time periods. Thus, it is extremely prone to bearing wear failures, and this can cause the whole generator set to fail to work smoothly. This paper takes wind turbine bearings as the research object and provides an overview and analysis for realizing fault warnings, avoiding bearing failure, and prolonging bearing life. Firstly, a study of the typical failure modes of wind turbine bearings was conducted to provide a comprehensive overview of the tribological problems and the effects of the bearings. Secondly, the failure characteristics and diagnosis procedure for wind power bearings were examined, as well as the mechanism and procedure for failure diagnosis being explored. Finally, we summarize the application of fault diagnosis methods based on spectrum analysis, wavelet analysis, and artificial intelligence in wind turbine bearing fault diagnosis. In addition, the directions and challenges of wind turbine bearing failure analysis and fault diagnosis research are discussed. [ABSTRACT FROM AUTHOR]

The occurrence of the motor shaft voltage and bearing current caused by the inverter will aggravate bearing damage and lead to the premature failure of bearings. Many types of equipment are being shut down due to bearing currents, such as filters, insulated bearings and grounding brushes. Traditional suppression measures cannot eliminate the bearing current and the bearing damage mechanism under the bearing current is not clear. In this paper, the damage caused by the bearing current to bearings is analyzed in detail. The influences of different working conditions on the bearing current and the damage caused are discussed. The source of bearing currents is introduced and the bearing current model under different working conditions is reviewed. An outlook for future studies is proposed, based on the current research status and challenges. [ABSTRACT FROM AUTHOR]

Abstract Background We present two cases of unicompartmental knee arthroplasty (UKA) bearing failure in this report—one case of bearing dislocation and one case of bearing fracture. The causes of failure in both cases are evaluated in depth and recommendations are provided regarding intraoperative technique to reduce risk of bearing failure in mobile bearing UKAs. Case presentation In the first case, intraoperative evidence of metallosis and chronic pain preceding the traumatic event may indicate that the patient had attenuation of her collateral ligaments that precipitated the instability event. In the second case, the relatively atraumatic nature of the bearing fracture-dislocation and intraoperative evidence of extensive poly wear suggest that the bearing fracture was likely due to a 3-mm bearing selection in the initial surgery. Conclusions This case report shows that late bearing in mobile bearing unicompartmental knee arthroplasty can often be a multifactorial event and treatment must address all the risk factors that led to bearing dislocation. Bearing fracture is a very rare complication associated with mobile bearing UKA and patients with thin polyethylene inserts are at risk for bearing fracture even in the absence of poly wear.

To address the challenges posed by early weak fault signals hindering fault feature extraction and leading to diminished recognition accuracy in deep learning models, we present a method for diagnosing bearing faults. This method integrates the Pelican Optimization Algorithm (POA), Variational Mode Decomposition (VMD), Gramian Angular Difference Fields (GADF), and the Swin Transformer network. Initially, the VMD's key parameter are optimized through POA to dynamically yield the optimal parameter combination. Subsequently, the refined VMD is employed to decompose the original signal. The intrinsic modal function (IMF) is subsequently filtered, and the signal is reconstructed using the weighted composite kurtosis(WCK) index. Following this step, the reconstructed 1D signal is converted into a 2D image using GADF and is then input into the constructed Swin Transformer transfer learning network model for the purpose of training. In conclusion, a fault diagnosis experiment was conducted using the measured signal, which showed the successful extraction of the signal's characteristics and octave frequencies through the method presented in this paper. Moreover, concerning fault identification, an identification accuracy exceeding 95% was achieved. • Using POA algorithm to select VMD parameters. • A new composite signal screening index has been proposed. • Image conversion using the GADF algorithm. • A Bearing Fault Recognition Model Combining transfer Learning and Swin Transformer. [ABSTRACT FROM AUTHOR]

• A mathematical model is presented for the mesh analysis within gear-shaft-bearing systems. • The coupling relationship and interaction between bearings and gear pairs in gearbox are revealed. • The calculation method for real-time mesh stiffness of gear pairs in complex environment is developed. • The influence mechanism of bearing failures on the mesh characteristics and contact state of gear pairs is explained. • The optimal modification strategy for gear pairs is determined based on the principle of minimum vibration response. To investigate the influence of bearing failures on the mesh characteristics of gear pairs, this paper presents a mathematical model for gear-shaft-bearing systems. Within this simulation method, the model of defective bearings and the contact analysis methodology of gear pairs are integrated into the system equations. Additionally, the coupling mechanism between system components is determined by addressing the misaligned shafts induced by asynchronous bearing vibrations. Furthermore, the torsional deformation of gear teeth generated by uneven load distribution is considered, and a calculation method for mesh stiffness in complex environments is developed. By employing the comprehensive model, the effect of raceway failures on the mesh behavior is lucidly explained, the real-time distribution of contact stress and load on the tooth surface can be obtained, and the optimal modification strategies for gears are formulated. Finally, the proposed model is confirmed through the finite element (FE) model and experimental observation, which provides a potent tool for the performance optimization of gearboxes. [ABSTRACT FROM AUTHOR]

Bearing failure often occurs in a doubly fed induction generator. The fault diagnosis method based on the current signals has been attracted much attention. In this article, we propose different discrete digital models and their measure functions employing random theory. First, based on the raw current signals with the probability density function (PDF), a distributed discrete digital model is proposed; to avoid finding the PDF in the raw current signals, a discrete digital model of the moment feature and a discrete digital model of raw data are proposed. Then, six measurement functions are proposed as features of the discrete digital model for pattern recognition and condition monitoring of bearing. Finally, the effectiveness of the current method is demonstrated by comparing different signal processing methods. [ABSTRACT FROM AUTHOR]

The reliability of rolling bearings is affected by white etching crack (WEC) or white structure flaking (WSF) failures, causing tremendous commercial burdens for bearing manufacturers and operators. The research for the underlying failure mechanism has attracted interest from a large scientific community over decades. Despite the significant amount of efforts, a root cause of white etching cracking is still missing. Amongst other factors, lubricant chemistry is considered to be essential in WEC formation. The authors aim to elucidate this key parameter by provoking white etching crack formation on a FE8 bearing test rig using a well-described set of chemicals in high- and low-reference lubricants. Scanning electron microscopy and energy dispersive X-ray analysis prove the presence of a patchy tribofilm on the surface of bearing washers, leading most likely to a higher frictional torque at the early stages of operation when the low reference oil is used. Secondary neutral mass spectrometry (SNMS) shows a hydrogen containing tribofilm in the shallow subsurface of about 30 nm depth, suggesting that hydrogen proliferating into bearing material may subsequently facilitate crack propagation via dislocation pileups, leading to premature bearing failure. [ABSTRACT FROM AUTHOR]

Fault signal of rolling bearing was easily submerged in strong background noise during actual operation, which made it difficult to identify the fault type. Aiming at these problems, a joint noise reduction method based on intrinsic time-scale decomposition (ITD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) was proposed, and applied to the fault diagnosis of rolling bearing. Firstly, the ITD algorithm was used to decompose the original signal of the rolling bearing fault to obtain multiple proper rotation components (PRC) ;Secondly, according to the principle of correlation coefficient and kurtosis, the PRC that had a greater correlation with the original signal was selected for reconstruction;Then, MOMEDA algorithm was used to further denoise the reconstructed signal to separate the useful signal from the noise signal. Finally, the envelope demodulation analysis of the signal was performed to extract the fault characteristic frequency and diagnose the specific location of the bearing fault. In addition, in order to verify the effectiveness of the method, the simulation signals were compared and analyzed by ITD and local mean value decomposition (LMD), MOMEDA and maximum correlation kurtosis deconvolution (MCKD), and the analysis of the outer ring instance was presented. The results indicate that the diagnosis acuracy of the joint noise reduction method based on ITD-MOMEDA is 4. 3% higher than the ITD-MCKD diagnosis accuracy, which can more effectively remove strong noise and successfully detect the type of bearing failure. [ABSTRACT FROM AUTHOR]

On the basis of the experimental results in previous work, a multiscale numerical modeling strategy on the failure behaviour of three-dimensional orthogonal woven carbon/carbon composites under pin-loading was presented. In consideration of the complexity of internal woven architecture, the finite element analysis at micro-/meso-/macro-scale levels was performed to exhibit the effective material properties and mechanical behaviour. The geometric models were reconstructed via X-ray tomography technology to obtain feasible configurations based on actual microstructure, meanwhile the models considered the fibers random arrangement in the yarn and voids stochastic distribution in the matrix. The anisotropic composites damage evolution was characterized by Murakami-Ohno damage theory. Additionally, for a further exploration on the practical bearing failure mode, the macro-scale open-hole plate model was established using mesh superposition method to expose the damage mechanism of each component in composites at hole edge, and the numerical predictions agreed reasonably well with the experimental results. [ABSTRACT FROM AUTHOR]

Wind turbine drivetrain bearing failures continue to lead to high costs resulting from turbine downtime and maintenance. As the standardised tool to best avoid downtime is online vibration condition monitoring, a lot of research into improving the signal analysis tools of the vibration measurements is currently being performed. However, failures in the main bearing and planetary gears are still going undetected in large numbers. The available field data is limited when it comes to the properties of the stored measurements. Generally, the measurement time and the covered frequency range of the stored measurements are limited compared to the data used in real-time monitoring. Therefore, it is not possible to either reproduce the monitoring or to evaluate new tools developed through research for signal analysis and diagnosis using the readily available field data. This study utilises 12 bearing failures from wind turbine condition monitoring systems to evaluate and make recommendations concerning the optimal properties in terms of measurement time and frequency range the stored measurements should have. The results show that the regularly stored vibration measurements that are available today are, throughout most of the drivetrain, not optimal for research-driven postfailure investigations. Therefore, the storage of longer measurements covering a wider frequency range needs to begin, while researchers need to demand this kind of data. [ABSTRACT FROM AUTHOR]

Wind turbines are widely installed as the new source of cleaner energy production. Dynamic and random stress imposed on the generator bearing of a wind turbine may lead to overheating and failure. In this paper, a data-driven approach for condition monitoring of generator bearings using temporal temperature data is presented. Four algorithms, the support vector regression machine, neural network, extreme learning machine, and the deep belief network are applied to model the bearing behavior. Comparative analysis of the models has demonstrated that the deep belief network is most accurate. It has been observed that the bearing failure is preceded by a change in the prediction error of bearing temperature. An exponentially-weighted moving average (EWMA) control chart is deployed to trend the error. Then a binary vector containing the abnormal errors and the normal residuals are generated for classifying failures. LS-SVM based classification models are developed to classify the fault bearings and the normal ones. The proposed approach has been validated with the data collected from 11 wind turbines.

The occurrence of the motor shaft voltage and bearing current caused by the inverter will aggravate bearing damage and lead to the premature failure of bearings. Many types of equipment are being shut down due to bearing currents, such as filters, insulated bearings and grounding brushes. Traditional suppression measures cannot eliminate the bearing current and the bearing damage mechanism under the bearing current is not clear. In this paper, the damage caused by the bearing current to bearings is analyzed in detail. The influences of different working conditions on the bearing current and the damage caused are discussed. The source of bearing currents is introduced and the bearing current model under different working conditions is reviewed. An outlook for future studies is proposed, based on the current research status and challenges.

Bearings are crucial components that decide whether or not a wind turbine can work smoothly and that have a significant impact on the transmission efficiency and stability of the entire wind turbine’s life. However, wind power equipment operates in complex environments and under complex working conditions over long time periods. Thus, it is extremely prone to bearing wear failures, and this can cause the whole generator set to fail to work smoothly. This paper takes wind turbine bearings as the research object and provides an overview and analysis for realizing fault warnings, avoiding bearing failure, and prolonging bearing life. Firstly, a study of the typical failure modes of wind turbine bearings was conducted to provide a comprehensive overview of the tribological problems and the effects of the bearings. Secondly, the failure characteristics and diagnosis procedure for wind power bearings were examined, as well as the mechanism and procedure for failure diagnosis being explored. Finally, we summarize the application of fault diagnosis methods based on spectrum analysis, wavelet analysis, and artificial intelligence in wind turbine bearing fault diagnosis. In addition, the directions and challenges of wind turbine bearing failure analysis and fault diagnosis research are discussed.

Dynamic simulations are often used to evaluate the vibrational response of rolling-element bearings experiencing defects. Previously, the optimal accelerometer position has been found to be as close as possible to the bearing. However, further details about the influence of rotational symmetry have not been closely investigated. This paper presents a dynamic simulation model of a radially loaded complete spherical roller bearing with a defect in the inner ring and placed in a housing with 72 equally spaced accelerometer positions around the circumference. Surface accelerations have been extracted and transformed into the frequency domain. Thereafter, the vibrational components indicating the defect have been evaluated around the circumference. The results show an optimal position as close as possible to the primary loaded zone and validation test rig experiments show a reasonable qualitative agreement. [ABSTRACT FROM AUTHOR]

The connections play an important role in the overall behaviour of the structures. Therefore an adequate structural design taking into account both safe and economic criterion is necessary. The structural design of stainless steel bolted connections using actual codes can provide inadequate design capacity mainly due to the use of similar rules based on analogies from mild carbon structures. Thus, the purpose of this paper is to evaluate the structural response of stainless bolted connections considering the steel grade austenitic 304. Experimental and numerical analyses have been carried out considering bolted connection under bearing failure. In details, the study cases were subjected to two shear planes with thin‐plate and thick‐plate. In these cases, the occurrence of curling failures is possible. A detailed study involving the geometric parameters adopted by design equations of bolted stainless steel connections was made. The outcomes showed a reduction of the ultimate capacity characterised by out‐plane deformation. In addition, the current codes led to significant difference when compared to both experimental and numerical results. [ABSTRACT FROM AUTHOR]

Bearing failure often occurs in rotating machinery. Fault diagnosis method based on vibration signals has been studied for many years. Considering complementary information of the vibration signals from different directions, this article proposed an applied model of a correlation probability box based on G-Copula function for diagnosing bearing faults. First, to avoid constructing binary Copula function directly from the definition of binary Copula function, a new function is defined, and a construction method of binary G-Copula function is proposed based on the new function. Then, the correlation probability box model is established based on a joint cumulative distribution of the G-Copula function to increase the independent of the input data in the support vector machine (SVM) model, and the aggregated widths of the correlation probability box model can be used to monitor a development of the bearing failure. Finally, the experimental results showed that the proposed method obtain the better classification accuracy than other data processing study.

The utilization of dual mobility (DM) articulations in total hip arthroplasty (THA) is increasing. The principal appeal of DM implants is its ability to reduce postoperative instability by maximizing the effective ball head size for each reconstruction. However, while DM implants have been used worldwide for over 3 decades, the experience in North America is more limited. Moreover, there remains concerns with intraprosthetic dissociation, wear, metallosis, and soft tissue impingement. Therefore, the purpose of this article is to review the available evidence for these potential issues. First, intraprosthetic dissociation (IPD) is a unique complication of DM implants. Although the rate has decreased with improvements in materials and design, the reported prevalence is approximately 1%. Second, wear in DM implants can be unpredictable and increased wear has been reported in younger, active patients. Third, corrosion in modular DM implants has been described and elevations in serum cobalt and chromium levels have been reported. While the clinical significance of these elevations is unclear, it remains a source of concern with these implants. Finally, psoas impingement and entrapment can be a source of persistent groin pain after THA. DM articulations are a valuable addition to the armamentarium of total hip surgeons. However, these bearings are not free of complications. Consequently, current data only support selective use of DM bearings in patients at increased risk for postoperative instability after arthroplasty. [ABSTRACT FROM AUTHOR]

This work shows the results of the comparative study of characteristic frequencies in terms of Power Spectral Density (PSD) or RMS generated by a blower unit and the SKFNU322 bearing. Data is collected following ISO 10816, using Emonitor software and with speed values in RMS to avoid high and low frequency signal masking. Bearing failure is the main cause of operational shutdown in industrial sites. The difficulty of prediction is the type of breakage and the high number of variables involved. Monitoring and analysing all the variables of the SKFNU322 bearing and those of machine operation for 15 years allowed to develop a new predictive maintenance protocol. This method makes it possible to reduce from 6 control points to one, and to determine which of the 42 variables is the most incidental in the correct operation, so equipment performance and efficiency is improved, contributing to increased economic profitability. The tests were carried out on a 500 kW unit of power and It was shown that the rotation of the equipment itself caused the most generating variable of vibrational energy. [ABSTRACT FROM AUTHOR]

Bearing damage is one of the common failure modes in composite bolted joints. This paper describes the development of an on-site monitoring method based on eddy current (EC) sensing film to monitor the bearing damage in carbon fiber reinforced plastic (CFRP) single-lap bolted joints under tensile testing. Configuration design and operating principles of EC array sensing film are demonstrated. A series of numerical simulations are conducted to analyze the variation of EC when the bearing failure occurs around the bolt hole. The results of damage detection in the horizontal direction and through the thickness direction in the bolt hole with different exciting current directions are presented by the finite element method (FEM). Experiments are performed to prove the feasibility of the proposed EC array sensing film when the bearing failure occurs in CFRP single-lap bolted joints. The results of numerical simulations and experiments indicate that bearing failure can be detected according to the variation of EC in the test specimen. [ABSTRACT FROM AUTHOR]

The reliability of rolling bearings is affected by white etching crack (WEC) or white structure flaking (WSF) failures, causing tremendous commercial burdens for bearing manufacturers and operators. The research for the underlying failure mechanism has attracted interest from a large scientific community over decades. Despite the significant amount of efforts, a root cause of white etching cracking is still missing. Amongst other factors, lubricant chemistry is considered to be essential in WEC formation. The authors aim to elucidate this key parameter by provoking white etching crack formation on a FE8 bearing test rig using a well-described set of chemicals in high- and low-reference lubricants. Scanning electron microscopy and energy dispersive X-ray analysis prove the presence of a patchy tribofilm on the surface of bearing washers, leading most likely to a higher frictional torque at the early stages of operation when the low reference oil is used. Secondary neutral mass spectrometry (SNMS) shows a hydrogen containing tribofilm in the shallow subsurface of about 30 nm depth, suggesting that hydrogen proliferating into bearing material may subsequently facilitate crack propagation via dislocation pileups, leading to premature bearing failure.

The highest costs due to premature failures in wind turbine drivetrains are related to defects in the gearbox, with bearing failures being overrepresented. Vibration monitoring has been identified as the primary tool to detect and diagnose these types of failures. However, late or no signs of the failures are still being reported. Artificial neural networks (ANNs) has been shown to favourably be used as a classifier of bearing failures to increase the detection and diagnosis performance, which requires labelled data when training for all types of considered failures. However, less work has been done with an ANN used to create descriptive functions of the vibration and turbine operation data relationship and thereby negating inherent variance in the vibration data and increasing the detectability when a defect appears. Therefore, this study utilizes the relationship between the rotational speed recorded during a vibration measurement and the calculated condition indicator values of specific bearing failures in three wind turbine gearbox failures. An ANN establishes a function between the rotational speed and condition indicator values with healthy training data collected before the failure occurred. Thereafter, whole datasets leading up to the changing of the gearboxes is used to predict the condition indicator values without the failure influence. The difference between the predicted and true values show an increased sensitivity of the detection in two cases of gearbox output shaft bearing failures as well as indicating a planet bearing failure which with the previous data had gone undetected. [ABSTRACT FROM AUTHOR]

Vibration-condition monitoring aims to detect bearing damages of rotating machinery's incipient failures mainly through time–frequency methods because of their efficient analysis of nonstationary signals. However, by having failures with impulse behavior, short-term events have a tendency to be diluted under variable-speed conditions, while information on frequency changes tends to be lost. Here, we introduce an approach to highlighting bearing impulsive failures by measuring short-term spectral components to deal with variable-speed vibrations. The short-term estimator employs two sliding windows: a small one that measures the instantaneous amplitude level and tracks impulsive components and a large interval that evaluates the average background amplitude. Aiming to characterize cyclo-non-stationary processes with impulsive behavior, the emphasizing high-order-based estimator based on the principle of spectral entropy is introduced. For evaluation, both visual inspection and classifier performance are assessed, contrasting the spectral-entropy estimator with the widely used spectral-kurtosis approach for dealing with impulsive signals. The validation of short-time/-angle spectral analysis performed on three datasets at variable speed showed that the proposed spectral-entropy estimator is a promising indicator for emphasizing bearing failures with impulse behavior. [ABSTRACT FROM AUTHOR]