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Bearing fault diagnosis is a well-developed field and an active area of research in which the combination of model-free machine learning techniques with vibration data has become a popular approach. However, vibration data from rotating machines has the potential to contain domain shifts beyond the accepted causes in this research area (different part models, operating conditions and sensor locations) which can enable data leakage between training and test datasets. To demonstrate the impact of data leakage, six common bearing diagnosis methods are applied to two datasets using three data splitting methods to compare classification performance. Diagnosis is preformed using Principal Component Analysis (PCA), Supervised Principal Component Analysis (SPCA) and Linear Discriminant Analysis (LDA) in combination with frequency analysis and envelope analysis feature extraction methods. Datasets from McMaster University and Paderborn University are used as experimental data sources, and produce vastly differing results (over a 40% drop in accuracy) depending on the selected dataset splitting method, revealing a previously unknown domain shift. Despite great results for diagnosis methods using frequency response analysis on the data from McMaster, these results are not expected to generalize due to possible data leakage. Out of fifty-five previous works using the Paderborn dataset, ten are identified as likely to be affected and only six properly address the problem. Recommendations are given for future experiment design, model creation and model evaluation.

Recently, the motor drive system of electric vehicles (EVs) has been increased to high voltage to enhance the output and efficiency of the motor. However, higher voltage exacerbates common-mode voltage (CMV), which is the main cause of bearing faults. Due to the CMV component, shaft voltage is generated in the motor, shortening the life of the bearing and, in severe cases, causing serious motor faults due to bearing damage. Therefore, it is very important to reduce the shaft voltage of the motor. This paper proposes a method for reducing shaft voltage by applying a shield ring with a novel structure to the stator part of the interior permanent magnet synchronous motor (IPMSM). First, the parasitic capacitance and shaft voltage equivalent circuit model of the IPMSM are analyzed. The effect of shaft voltage based on the material of the shield ring is then analyzed. In addition, changes in shaft voltage magnitude are analyzed according to the shape of the shield ring. Finally, the proposed method is verified through experiments.

For the force problem of the flexible thin-walled bearing in harmonic reducers, the inadequacy of the previous single-factor analysis of the bearing is analyzed； and in order to explore the influence of other components on the bearing force of the shaft, theoretical analysis is conducted through the whole machine model, the deformation of the flexible wheel and the coordination process of the raceway roller deformation are explored, and the force state of the thin-walled flexible bearing is obtained as a whole. By verifying the stress and deformation of the whole machine model by Workbench software, the results show that the force state of the bearing simulation is consistent with the theoretical calculation.

Purposes Bearing is one of the core components in the manufacturing industry. Its health status determines the safety of the host. Short-term failure prediction can effectively ensure the smooth progress of the industrial production process. Methods In order to solve the end-to-end problem, a temporal convolutional network (TCN) based short-term fault prediction strategy was proposed. The network could directly output the types of failure that would eventually occur in the bearing and the degradation stage that would be in the next moment through the data monitored at the current moment. In addition, soft threshold with attention mechanism is proposed to solve the problem of background noise in the working environment of bearings or noise interference in the process of data acquisition. During the short-term fault prediction process, the attention mechanism adaptively generates a soft threshold according to the prediction target of the TCN network, and the soft threshold acts on the spatiotemporal features extracted by the TCN to achieve the purpose of reducing noise impact. Findings The experimental results show that the proposed algorithm has high accuracy, which verifies the effectiveness and high practical engineering application value of the proposed algorithm.

This paper introduces a novel diagnostic approach for bearing ball failures: a synergistic implementation of a bidirectional Long Short-Term Memory (LSTM) network, empowered by Gradient Minimum Mean Square. This method leverages deep analysis of operational data from bearings, enabling the precise identification of incipient bearing ball failures at early stages, thus markedly improving prediction accuracy. Our empirical results underscore the superior performance of this composite methodology in accurately detecting a spectrum of five mechanical bearing ball failure types, achieving a substantial enhancement in diagnostic precision.

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

Convolutional neural networks (CNNs) serve as powerful feature extraction tools capable of effectively extracting information from complex environments, thus improving the accuracy of fault identification for bearing data. In this paper, we present a method for diagnosing bearing faults using an attention mechanism and a multi-scale convolutional neural network (MSCNN). Firstly, truncate and sample the rolling bearing data, and use continuous wavelet transform to generate corresponding time-frequency images, which will be used as inputs to the neural network. Next, the MSCNN, which includes efficient convolutional modules with residual structures, is utilized to extract features from the input data while maximizing the retention of valuable information. The extracted data then undergoes feature selection through the employment of an Efficient Convolutional Module (ECM) with channel attention. Finally, after being mapped through fully connected layers, the features are fed into a softmax layer for fault category prediction. In this study, the model results were tested and verified using the Case Western Reserve University (CWRU) dataset and the bearing dataset of Jiangnan University(JNU). A comparison was made with the LeNet model, ResNet model, LSTM model, and WDCNN model. The results showed that the classification accuracy of the ten types of bearing signals at the same speed can reach 100%, and the classification accuracy of the thirty types of bearing signals at different speeds can reach over 99.4%, significantly higher than the other models. The proposed method achieves the recognition of different fault states of rolling bearings under complex conditions, including multiple operating conditions and variable operating conditions. It is capable of extracting the global characteristic information of bearing faults, resulting in high diagnostic accuracy and good generalization ability. This method can provide reference for the diagnosis of rolling bearing faults under corresponding operating conditions.

Predicting the remaining useful life (RUL) of mechanical bearings is crucial in the industry. Estimating the RUL enables the assessment of health bearing, maintenance planning, and significant cost reduction, thereby fostering industrial development. Existing models rely on traditional feature engineering with feature changes because operating conditions pose a major challenge to the generalization of RUL prediction models. This study focuses on neural network-based feature engineering and the downstream prediction of the RUL, eliminating the need for specific prior knowledge and simplifying the development and maintenance of models. Initially, a convolutional neural network (CNN) model is employed for feature engineering. Subsequently, a bidirectional long short-term memory network (Bi-LSTM) model is used to capture the time-series degradation characteristics of the engineered features and predict the RUL through regression. Finally, the study examines the influence of operating conditions in the model and integrates domain adaptation to minimize differences in feature distribution, thereby enhancing the model’s generalizability for the RUL prediction.

Bearings are the most vulnerable component in low-voltage induction motors from a maintenance standpoint. Vibration monitoring is the benchmark technique for identifying mechanical faults in rotating machinery, including the diagnosis of bearing defects. The study of different bearing fault phenomena under induction motor transient conditions offers interesting capabilities to enhance classic fault detection techniques. This study analyzes the low-frequency localized bearing fault signatures in both the inner and outer races during the start-up and steady-state operation of inverter-fed and line-started induction motors. For this aim, the classic vibration envelope spectrum technique is explored in the time–frequency domain by using a simple, resampling-free, Short Time Fourier Transform (STFT) and a band-pass filtering stage. The vibration data are acquired in the motor housing in the radial direction for different load points. In addition, two different localized defect sizes are considered to explore the influence of the defect width. The analysis of extracted low-frequency characteristic frequencies conducted in this study demonstrates the feasibility of detecting early-stage localized bearing defects in induction motors across various operating conditions and actuation modes.

The majority of bearings in the world are lubricated by grease, and nearly 80% of premature bearing damage is attributed to lubrication issues. Accurate measurement and prediction of film thickness are crucial aspects of understanding the lubrication mechanism in grease-lubricated bearings. This work focuses on grease film thickness measurement using the capacitance method in real bearings. It comprehensively reviews the current status, identifies key challenges, and proposes solutions. Mechanisms of mainstream electronic components in capacitance measurement were reviewed for the first time. It enables more accurate capacitance measurement. A new capacitive model and electric network to measure film thickness in fully flooded, starved, and mixed regimes are developed. It is more comprehensive compared to current models. Classic dielectric models are reviewed, and suitable ones for lubricants are proposed. It facilitates a more precise film thickness measurement. Finally, a new grease film thickness model (bearing raceway) is proposed based on the 113 literature capacitive film thickness data points from five different authors. The satisfied R-squared value indicates a strong correlation.

In response to the challenges of bearing fault diagnosis under small sample sizes and variable operating conditions, this paper proposes a novel method based on the two-dimensional analysis of vibration acceleration signals and a Multi-Task Conditional Generative Adversarial Network (MTC-GAN). This method first constructs two-dimensional images of vibration signals by leveraging the physical properties of the bearing acceleration signals and employs Local Binary Patterns (LBP) to extract subtle texture features from these images, thereby generating fault feature signatures with high discriminative power across different operating conditions. Subsequently, MTC-GAN is utilized for data augmentation, and the trained discriminator is used to perform fault classification tasks, improving classification accuracy under conditions with small sample sizes. Experimental results demonstrate that the proposed method achieves excellent fault diagnosis accuracy and robustness under both small sample sizes and varying operating conditions. Compared to traditional methods, this approach exhibits higher efficiency and reliability in handling complex operating conditions and data scarcity.

Machine learning (ML) and artificial intelligence (AI) have emerged as the most advanced technologies today for solving issues as well as assessing and forecasting occurrences. The use of AI and ML in various organizations seeks to capitalize on the benefits of vast amounts of data based on scientific approaches, notably machine learning, which may identify patterns of decision-making and minimize the need for human intervention. The purpose of this research work is to develop a suitable neural network model, which is a component of AI and ML, to assess and forecast crack propagation in a bearing with a seeded crack. The bearing was continually run for many hours, and data were retrieved at time intervals that might be utilized to forecast crack growth. The variables root mean square (RMS), crest factor, signal-to-noise ratio (SNR), skewness, kurtosis, and Shannon entropy were collected from the continuously running bearing and utilized as input parameters, with the total crack area and crack width regarded as output parameters. Finally, utilizing several methodologies of the Neural Network tool in MATLAB, a realistic ANN model was trained to predict the crack area and crack width. It was observed that the ANN model performed admirably in predicting data with a better degree of accuracy. Through analysis, it was observed that the SNR was the most relevant parameter in anticipating data in bearing crack propagation, with an accuracy rate of 99.2% when evaluated as a single parameter, whereas in multiple parameter analysis, a combination of kurtosis and Shannon entropy gave a 99.39% accuracy rate.

With the increasing complexity of mechanical equipment and diversification of deep learning models, vibration signals collected from such equipment are susceptible to noise interference. Moreover, traditional neural network models struggle to be effectively deployed in production environments with limited computational resources, severely impacting the accurate extraction and effective diagnosis of FK fault characteristics. In response to this challenge, this study proposes a fault diagnosis method for rolling bearings, integrating a lightweight ShuffleNetV2 network with variational mode decomposition (VMD) and the fast kurtogram (FK) algorithm. Initially, this paper introduces an enhanced FK method where the VMD algorithm is employed for data denoising, extracting FK post-denoising. These feature maps not only preserve critical signal information but also simplify data complexity. Subsequently, these feature maps are utilized to train and test the ShuffleNetV2 model, facilitating effective fault identification and classification. Ultimately, by conducting experimental comparisons with several mainstream lightweight network models, such as MobileNet and SqueezeNet, as well as traditional convolutional neural network models, this study validates the effectiveness of the proposed method in extracting fault characteristics from vibration signals, demonstrating superior diagnostic accuracy and computational efficiency. This provides a novel technical approach for health monitoring and fault diagnosis of industrial bearings and offers theoretical and experimental support for the deployment of lightweight networks in industrial applications.

This work addresses the rolling element bearing (REB) fault classification problem by tackling the issue of identifying the appropriate parameters for the extreme learning machine (ELM) and enhancing its effectiveness. This study introduces a memetic algorithm (MA) to identify the optimal ELM parameter set for compact ELM architecture alongside better ELM performance. The goal of using MA is to investigate the promising solution space and systematically exploit the facts in the viable solution space. In the proposed method, the local search method is proposed along with link‐based and node‐based genetic operators to provide a tight ELM structure. A vibration data set simulated from the bearing of rotating machinery has been used to assess the performance of the optimized ELM with the REB fault categorization problem. The complexity involved in choosing a promising feature set is eliminated because the vibration data has been transformed into kurtograms to reflect the input of the model. The experimental results demonstrate that MA efficiently optimizes the ELM to improve the fault classification accuracy by around 99.0% and reduces the requirement of hidden nodes by 17.0% for both data sets. As a result, the proposed scheme is demonstrated to be a practically acceptable and well‐organized solution that offers a compact ELM architecture in comparison to the state‐of‐the‐art methods for the fault classification problem. [ABSTRACT FROM AUTHOR]

Bearing is the most vulnerable key part in rotating machine and bears important influence on the safety of equipment. Weakness and complexity are the two features of fault characteristic information carried by signals in the early stage of fault. For that, a fault is difficult to be recognized correctly. To identify a compound failure of bearing, the paper has brought forward a new self-adaptive option method for component signals that are sensitive to failure feature information of bearing. The sensitivity of kurtosis to bearing failure is exploited and the influence of signal complexity on the extraction of failure feature information is taken seriously, the paper has proposed the self-adaptive option method for component signals that are sensitive to failure feature by combined kurtosis with Complexity parameter included in Hjorth parameters. Furthermore, as the mid-value represents the general level of signal and is not affected by larger or smaller data, with the mid-values of kurtosis and Complexity parameter as the boundary, the paper has chosen the component signals which can more comprehensively show the failure features of bearing. Additionally, by principal component analysis (PCA), component signals selected are blended and reconstructed. Finally, by the Hilbert envelope spectrum of signals reconstructed, failure types of bearing are identified. To verify the effectiveness of presented method, the presented method is compared with conventional method on the basis of the exactly consistent data. The result indicates that the proposed method is superior to the traditional one in extracting fault information and identifying the multiple failure types of bearing. [ABSTRACT FROM AUTHOR]

The noise associated with ceramic-on-ceramic (CoC) total hip arthroplasty (THA) has been a concerning issue, while its underlying causes remain unclear. We conducted a retrospective analysis of 119 patients (174 primary CoC THAs) who had a mean follow-up of 28 months (range, 12 to 106). A questionnaire was designed to collect information on nature, frequency, onset, duration, and impact of the noise. Postoperative x-rays were evaluated. Clinical evaluations, including Harris and Oxford hip scores, were documented at follow-up time points (6 weeks, 3 months, 6 months, and 1 year). Of the 174 hips, 31.6% reported noise, including 26 popping (14.9%), 24 clicking (12.1%), and 5 grinding (2.9%). No patients reported squeaking. Noisy hips had lower age (P =.009) and body mass index (P =.019). Among patients with developmental dysplasia of the hip, 17 of 55 hips reported noise associated with smaller cup anteversion angle (P =.004), greater body height (P =.022), and larger acetabular cup size (P =.049). Noise typically began at a mean of 193 days (range, 1 to 2,598) after surgery and disappeared spontaneously in 50.9% of hips before final follow-up, with an average disappearance time of 211 days (range, 60 to 730). Noise did not affect daily life in 74.5% of patients, while 26.9% of patients who had popping reported painful sensations. One patient experienced joint dislocation, and another experienced a ceramic liner fracture during follow-up. No statistical difference was observed in outcome scores between noise and silent groups at 4 follow-up time points. Incidence of noise after primary CoC THA is relatively high. Smaller cup anteversion and larger acetabular cup size were associated with noise production in patients who had developmental dysplasia of the hip. [ABSTRACT FROM AUTHOR]

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

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

In order to analyze the influence of voltage sag on gear and bearing of the doubly-fed wind turbine drivetrain, rotor finite element method and centralized parameter method were used to carry out dynamic modeling of the disk, shaft, bearing, coupling and gear. Combined with the aerodynamic calculation and control model, the dynamic response simulation program of drivetrain was established. The program was used to simulate the steady-state condition of drivetrain, and the components with the maximum gear meshing force and bearing force were selected. The curve obtained from test of a MW(megawatt) wind turbine were loaded into the dynamic simulation program, the dynamic response of the low voltage sag under various conditions were compared and analyzed. The results show that the voltage sag produces shock loads on the gears and bearings of drivetrain, the shock loads are related to the wind turbine load, voltage sag amplitude and sag form, and the influence factors of these three decreases in turn. [ABSTRACT FROM AUTHOR]

Carbon fiber–reinforced SiC ceramic matrix composites (C/C–SiC composites) with advantages of high wear resistance and corrosion resistance are of high application potential as ceramic bearings. In this study, the frictional and wear behaviors of C/C–SiC with different fiber orientations mating with Al2O3 and SiC balls, respectively, have been investigated. Results reveal that fiber orientation effectively affects the friction and wear properties of carbon–ceramic composites in paired friction experiments with ceramic balls. In comparison to C/C–SiC with unidirectional fiber orientation, C/C–SiC pads with randomly arranged fibers exhibited a more stable coefficient of friction and lower volume wear, presenting the best performance among the pads considered herein, which may have the potential to be applied to bearing materials. The factors affecting the formation of continuous tribo‐film, which influence the comprehensive performance, are researched. It is proposed that the amount of wear debris and the formation of SiC/SiC micro‐hard frictional pairs may inhibit or promote the formation of continuous friction film. The dominant wear mechanisms for friction pairs of C/C–SiC and ceramic are abrasive wear and oxidation wear. [ABSTRACT FROM AUTHOR]

Additive manufacturing facilitates the materialization of complex designs, e.g. bio-inspired non-assembly joints. Despite the advantages of this type of joints, such as single-step manufacturing, their performance under operational conditions remains poorly understood. This paper explores the design, fabrication and wear performance of a hinge-type joint inspired by the camel elbow. The joint was fabricated by metal additive manufacturing and its wear behaviour was numerically and experimentally evaluated. The results show that the bio-inspired design performs better than a cylindrical one in terms of wear distribution. This work helps to better understand the wear performance of non-assembled bio-inspired joints. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) for monitoring slowly evolving degradation processes typically involves overcoming data drift, complexity, and unavailability issues resulting from dynamic and harsh conditions and the rarity of labeled failure patterns, respectively. While degradation patterns are mostly hidden in such complex data, observation-based DL is prone to producing uncertain predictions and/or overfit models during the training process. This problem is usually caused by the insignificance of certain data representations. Therefore, and particularly due to the sequential nature of data in such a degradation process, it is necessary to consider neighboring observations to judge the accuracy of a representation or improve it. In this context, instead of providing traditional observation-based learning philosophy, this paper presents data-driven sequential mapping while additionally showing that health indices can also be represented as a vector of sequential data and not as a single regressor output changing a model's architecture. Using a dataset generated from a mathematical model mimicking bearing degradation life cycles and responding to the aforementioned three main challenges, a comparative study built on investigating observation-based and sequence-based learning paths was conducted. According to a well-defined visual and numerical evaluation criterion, a sequence-based methodology reflects a better understanding of data representations through parameter tuning, achieving better approximation and generalization. Such results support the necessity of such a learning mechanism, especially for sequential data, dealing with some sort of correlation and degrading controversy. The files required to reproduce the findings of this work have been made publicly available. [ABSTRACT FROM AUTHOR]

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Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The bearing is one of the most important components of rotating machinery. Predicting the remaining useful life (RUL) of bearings is of great importance to avoid unforeseen failures in the mechanical system and reduce the maintenance cost of the system. In conventional RUL prediction methods, the influence of noise and timing information on the prediction accuracy is often ignored. To address this problem, a method for RUL prediction of bearings based on data enrichment and adaptive temporal convolutional networks (TCN) is proposed in this paper. First, the sample entropy is used to optimize the singular spectrum decomposition (SSD) and combine with the correlation coefficient, and the data enhancement method based on the enhanced SSD and correlation coefficient (ISSD–CC) is proposed to reduce the signal noise; then, the time domain information is extracted to construct the feature vector. Finally, the Nadam optimizer and adaptive parametric rectified linear unit activation function (APRelu) are introduced, and an improved temporal convolutional network (ITCN) is proposed to improve the adaptive learning ability of TCN for time and time–domain information. IEEE PHM2012 dataset and the XJTU-SY dataset are used to verify the feasibility of the proposed method. The experimental results show that the accuracy of ISSD correlation coefficient and ITCN prediction method in RUL prediction of bearings is better than other comparison methods. [ABSTRACT FROM AUTHOR]

Copyright of Rolling Stock (1002-7602) is the property of Rolling Stock Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The use of anaerobic polymer compositions for the repair of precision shaft-bearing joints has currently been poorly studied; there are no proven recommendations for choosing the optimal thickness of the polymer layer, at which a significant decrease in the rigidity of the assembly will not occur. The article presents the results of a number of experiments, the purpose of which is to test the theoretical dependencies for determining the optimal thickness and characteristics of the intermediate polymer layer in the spindle-bearing connection. Computer modeling of the behavior of the shaft–polymer layer–ring model was carried out, which made it possible to confirm the adequacy of the mathematical model for determining the rigidity of the spindle assembly of a metal-cutting machine. A technique has been developed for experimental studies of the rigidity of the shaft–polymer–inner ring of a bearing joint. The results of studies of the displacement of a ring fixed on a shaft using Anaterm-112 anaerobic glue from a uniformly increasing radial load are presented. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI), which has recently gained popularity, is being extensively employed in modern fault diagnostic research to preserve the reliability and productivity of machines. The effectiveness of AI is influenced by the quality of the labeled training data. However, in engineering scenarios, available data on mechanical equipment are scarce, and collecting massive amounts of well-annotated fault data to train AI models is expensive and difficult. In response to the inadequacy of training samples, a numerical simulation-based partial transfer learning method for machinery fault diagnosis is proposed. First, a suitable simulation model of critical components in a mechanical system is developed using the finite element method (FEM), and numerical simulation is performed to acquire FEM simulation samples containing different fault types. Second, several synthetic simulation samples are generated to form complete source domain training samples using a generative adversarial network. Subsequently, the partial transfer learning network is trained to extract shared fault characteristics between the simulation and measured samples in the case of class imbalance. Finally, the resulting model is used to diagnose unknown samples from real-world mechanical systems in operation. The proposed method is tested on actual fault samples of bearings and gears obtained from a public dataset and experimental test rig available in our laboratory, achieving average classification accuracy of 99.54% and 99.64%, respectively. Comparison investigations reveal that the proposed method has superior classification and generalization ability when detecting faults in real mechanical systems. [ABSTRACT FROM AUTHOR]

Turbin Ulir Archimedes merupakan sebuah mekanisme pemanfaatan energi air pada head (tinggi jatuh air) yang rendah pada aliran sungai. Permasalahan yang ada pada turbin ulir tersebut adalah sulitnya pemasangan di lapangan karena ukuran yang cukup besar dan dipasang pada remote area. Salah satu solusi dari permasalahan ini adalah membuat turbin ulir dalam bentuk modular. Penelitian ini fokus pada desain dan pembuatan salah satu modul turbin ulir yaitu upper bearing. Komponen upper bearing terdiri dari poros atas, housing, cover, spacer, roller bearing, thrust bearing, dan seal. Poros atas, housing, cover, dan spacer merupakan komponen non standar yang dibuat dengan menggunakan mesin perkakas. Sedangkan roller bearing, thrust bearing, dan seal merupakan komponen standar pabrikan. Hasil perhitungan ukuran poros atas dan ukuran baut menunjukkan desain aman karena beban yang diterima masih dibawah kapasitas yang diijinkan. Masing-masing diameter poros terkecil adalah 20 mm dan ukuran baut M16 Grade 4.6. Material housing St37 juga cukup kuat untuk menerima beban geser. Keseluruhan komponen upper bearing berhasil dipasang dan diuji fungsi dengan baik. Poros dapat memutar tanpa ada gesekan yang berarti yang menunjukkan assembly modul upper bearing telah berhasil dibuat.

The cooling method for machining center spindles commonly involves utilizing a fixed-side jacket cooling system with lubricating oil or coolant as the refrigerant. The jacket cooling on the fixed side can be optimally designed so that the coolant flows where it is desired to be cooled. It should have a closed loop cooling circuit and a temperature control unit. On the other hand, since the spindle shaft rotates, it is necessary to cool the shaft by flowing a refrigerant from the fixed part to the rotating part. Cooling of the spindle shaft is expensive because it requires a complex mechanism such as a well-balanced high-precision component and a rotating joint for cooling. In addition, this cooling technology causes a loss of torque due to fluid resistance because the coolant flows through a shaft that rotates at high speed. Such a complicated and high-loss cooling method enables high precision and high speed. However, it is necessary to consider improvement. In recent years, based on such a demand, a cooling method using a heat exchanger (HE), which does not use refrigerant for the rotating spindle shaft, has been studied. In this study, the effects of the HE and jacket cooling were confirmed with spindles for machining centers. Specifically, by mounting a heat pipe (HP) in the spindle shaft that transfers heat to the HE at high speed, it was possible to reduce the temperature inside the bearing by 5 K and suppress thermal displacement by 47 %. On the other hand, the heat removal amount calculated from the temperature difference of the coolant for jacket cooling did not change with or without HEs. In other words, it was found that the HE can reduce the heat quantity by the bearing and the temperature of the shaft without increasing the jacket cooling capacity.

The detection of sudden faults in wind turbine generator (WTG) is a complex task, especially in bearings. Usually, the evaluation of methodologies such as vibration, ultrasound, and bearing temperatures are widely used in predictive maintenance, an important aspect for the traditional approach, in wind turbine fault detection, is the limited analysis with a single variable as vibration, or temperature. For instance, these sensors detect 5–20% of torsional vibration in the drivetrain and 55% has a failure due to lubricant problem, 20% for solid contamination or 9% for the incorrect application of the bearing. Consequently, to solve this limitation and failures modes, this research evaluated the limits and focused on the early detection of bearing faults in wind generators; it utilized a multi-stage approach involving Random Forest, XGBoost, Light XGB, and Logistic Regression, followed by probability scores and optimal features with a search grid validation, in addition, validated results through finite element modeling, Boroscopy, and vibration analysis. Hence, the database considers faults for bearings, gearboxes, and for normal operation; with the vibration analysis regarding 8,711,808 samples used for validating process. The result of the study is the detection of sudden failures five days before vibration analysis with high classification accuracy of 99.994%, recall of 99.982%, F1 score of 98.124%, kappa 99.330%, and a test set time of 22.82 s. This new approach provides an early detection of sudden failures compared to traditional vibration analysis in bearings and gearboxes.

ObjectivesDomain adaptive technology is widely used in the bearing fault diagnosis of variable operating conditions. However, most domain adaptive technology only focuses on the global domain distribution and ignores the subdomain distribution, and the domain-invariant feature quality is easily affected by noise, leading to a significant decrease in diagnostic accuracy under varying operation conditions. Therefore, a fault diagnosis method based on a self-attention subdomain adaptive adversarial network (SASAAN) is proposed.MethodsFirst, a convolutional block attention module (CBAM) is utilized to extract the fault-related domain-invariant features in the vibration signals of the source and target domains. The adversarial network and subdomain adaptive module are then combined to reduce differences in the global and local domain edge distributions of different operating condition data, thereby improving the transferability of the data. The loss function is optimized by back propagation using the Adam optimizer to improve the diagnostic performance of the model, and the hyperparameter tuning of the model is also performed. Finally，the diagnostic results on the target domain test set are output by the failure classifier, and the Ottawa bearing data set is used to validate the effectiveness of the proposed method. ,ResultsThe results show that the fault diagnosis accuracy of the proposed method is higher than 96% under the condition of strong noise and varying operation conditions, which is obviously better than other methods.ConclusionThe results of this study can provide valuable references for the fault diagnosis of rolling bearings under varying operation conditions.

This work presents an analysis of vibration signals for bearing defects using a proposed approach that includes several methods of signal processing. The goal of the approach is to efficiently divide the signal into two distinct components: a meticulously organized segment that contains relatively straightforward information, and an inherently disorganized segment that contains a wealth of intricately complex data. The separation of the two component is achieved by utilizing the weighted entropy index (WEI) and the SVMD algorithm. Information about the defects was extracted from the envelope spectrum of the ordered and disordered parts of the vibration signal. Upon applying the proposed approach to the bearing fault signals available in the Paderborn university database, a high amplitude peak can be observed in the outer ring fault frequency (45.9 Hz). Likewise, for the signals available in XJTU-SY, a peak is observed at the fault frequency (108.6 Hz).

Bearings are important components of rotating machinery and transmission systems, and are often damaged by wear, overload and shocks. Due to the low resolution of traditional time-frequency analysis for the diagnosis of bearing faults, a synchrosqueezed wavelet transform (SSWT) is proposed to improve the resolution. An improved convolutional neural network fault diagnosis model is proposed in this paper, and a Bayesian optimisation method is applied to automatically adjust the structure and hyperparameters of the model to improve the accuracy of bearing fault diagnosis. Experimental results from the accelerated life testing of bearings show that the proposed method is able to accurately identify various types of bearing fault and the different status of these faults under complex running conditions, while achieving very good generalisation ability.

The main cause of the surface wear of box-like parts of bearing interfaces is determined. According to the test results, an engineering solution for fretting corrosion control is proposed which consists in the friction application of copper films on the surface in contact with the bearing. [ABSTRACT FROM AUTHOR]

In the bearing-based formation control of autonomous surface vehicles, the scaling maneuver capability is greatly limited when faced with actuator faults and uncertainties. Under these circumstances, to better realize the formation scaling maneuver, a concise adaptive fault-tolerant formation scaling control scheme is proposed for autonomous vehicles with bearing measurements. By means of dynamic surface control, parameter integration and the adaptive technique, the tedious derivative calculation of virtual control signals is avoided and the prescribed formation scaling maneuver is achieved without knowing specific information about the faults and models. It is shown that both yaw angle tracking errors and bearing errors are able, ultimately, to be made uniformly bounded using this scheme. Meanwhile, only one control parameter and one adaptive parameter need to be updated during the formation scaling process. Stability analysis and comparative results are provided to verify the validity of the developed scheme.

This study investigates a novel approach for assessing the health status of rotating machinery transmission systems by analyzing the dynamic degradation of bearings. The proposed method generates multi-dimensional data by creating virtual states and constructs a multi-dimensional model using virtual state-space in conjunction with mechanism model analysis. Innovatively, the Hammerstein–Wiener (HW) modeling technique from control theory is applied to identify these dynamic multi-dimensional models. The modeling experiments are performed, focusing on the model’s input and output types, the selection of nonlinear module estimators, the configuration of linear module transfer functions, and condition transfer. Dynamic degradation response signals are generated, and the method is validated using four widely recognized databases consisting of accurate measurement signals collected by vibration sensors. Experimental results demonstrated that the model achieved a modeling accuracy of 99% for multiple bearings under various conditions. The effectiveness of this dynamic modeling method is further confirmed through comparative experimental data and signal images. This approach offers a novel reference for evaluating the health status of transmission systems.

To improve the performance of roller bearing fault diagnosis, this paper proposes an algorithm based on subtraction average-based optimizer (SABO), variational mode decomposition (VMD), and weighted Manhattan-K nearest neighbor (WMH–KNN). Initially, the SABO algorithm uses a composite objective function, including permutation entropy and mutual information entropy, to optimize the input parameters of VMD. Subsequently, the optimized VMD is used to decompose the signal to obtain the optimal decomposition characteristics and the corresponding intrinsic mode function (IMF). Finally, the weighted Manhattan function (WMH) is used to enhance the classification distance of the KNN algorithm, and WMH–KNN is used for fault diagnosis based on the optimized IMF features. The performance of the SABO–VMD and WMH–KNN models is verified through two experimental cases and compared with traditional methods. The results show that the accuracy of motor-bearing fault diagnosis is significantly improved, reaching 97.22% in Dataset 1, 98.33% in Dataset 2, and 99.2% in Dataset 3. Compared with traditional methods, the proposed method significantly reduces the false positive rate.

Bearings are one of the main components of induction motors, machines widely employed in today’s industries, making their monitoring a primordial task; however, most systems focus on measuring one physical magnitude to detect one kind of fault at a time. This research tackles the combination of two common faults, grease contamination and outer race damage, as lubricant contamination significantly impacts the life of the bearing and the emergence of other defects; as a contribution, this paper proposes a methodology for the diagnosis of this combination of faults based on a proprietary data acquisition system measuring vibration and current signals, from which time domain statistical and fractal features are computed and then fused using LDA for dimensionality reduction, ending with an SVM model for classification, achieving 97.1% accuracy, correctly diagnosing the combination of the contamination with different severities of the outer race damage, improving the classification results achieved when using vibration and current signals individually by 7.8% and 27.2%, respectively.