Search

The response of a nonlinear system excited by multi-frequency signals and Poisson white noise contains abundant dynamic behaviours. The study on system response has certain practical values. Utilising optimal dynamics response has unique advantages in weak feature identification under strong background noise. In this work, we study the optimal dynamics response in the bistable system excited by multi-frequency signals and Poisson white noise. The effects of noise parameters on system response and feature identification are discussed and analysed. Also, a signal identification method for unknown multi-frequency signals based on the optimal resonance response of the nonlinear system is proposed and applied to the compound fault diagnosis of rolling bearings, which utilises the difference between stochastic resonance and coherence resonance to determine characteristic components of the signals. The proposed method effectively identifies the characteristic components of the original excitation and avoids misjudgment caused by strong noise, which is effectively verified via simulation analysis and experiments. [ABSTRACT FROM AUTHOR]

Ensuring precise prediction of the remaining useful life (RUL) for bearings in rolling machinery is crucial for preventing sudden machine failures and optimizing equipment maintenance strategies. Since the significant interference encountered in real industrial environments and the high complexity of the machining process, accurate and robust RUL prediction of rolling bearings is of tremendous research importance. Hence, a novel RUL prediction model called CNN-VAE-MBiLSTM is proposed in this paper by integrating advantages of convolutional neural network (CNN), variational autoencoder (VAE), and multiple bi-directional long short-term memory (MBiLSTM). The proposed approach includes a CNN-VAE model and a MBiLSTM model. The CNN-VAE model performs well for automatically extracting low-dimensional features from time–frequency spectrum of multi-axis signals, which simplifies the construction of features and minimizes the subjective bias of designers. Based on these features, the MBiLSTM model achieves a commendable performance in the prediction of RUL for bearings, which independently captures sequential characteristics of features in each axis and further obtains differences among multi-axis features. The performance of the proposed approach is validated through an industrial case, and the result indicates that it exhibits a higher accuracy and a better anti-noise capacity in RUL predictions than comparable methods. [ABSTRACT FROM AUTHOR]

In mechanical equipment, rolling bearing components are constantly exposed to intricate and diverse environmental conditions, rendering them vulnerable to wear, performance degradation, and potential malfunctions. To precisely extract and discern rolling bearing vibration signals amidst intricate noise interference, this paper introduces a fault feature extraction and diagnosis methodology that seamlessly integrates an improved Fourier decomposition method (FDM), singular value decomposition (SVD), and maximum second-order cyclostationary blind convolution (CYCBD). Initially, the FDM is employed to meticulously decompose the bearing fault signals into numerous signal components. Subsequently, a comprehensive weighted screening criterion is formulated, aiming to strike a balance between multiple indicators, thereby enabling the selective screening and reconstruction of pertinent signal components. Furthermore, SVD and CYCBD techniques are introduced to carry out intricate processing and envelope demodulation analysis of the reconstructed signals. Through rigorous simulation experiments and practical rolling bearing fault diagnosis tests, the method's noteworthy effectiveness in suppressing noise interference, enhancing fault feature information, and efficiently extracting fault features is unequivocally demonstrated. Furthermore, compared to traditional time–frequency analysis methods such as EMD, EEMD, ITD, and VMD, as well as traditional deconvolution methods like MED, OMEDA, and MCKD, this method exhibits significant advantages, providing an effective solution for diagnosing rolling bearing faults in environments with strong background noise. [ABSTRACT FROM AUTHOR]

To address the problems of existing 2D image-based imbalanced fault diagnosis methods for rolling bearings, which generate images with inadequate texture details and color degradation, this paper proposes a novel image enhancement model based on a dual-branch generative adversarial network (GAN) combining spatial and frequency domain information for an imbalanced fault diagnosis of rolling bearing. Firstly, the original vibration signals are converted into 2D time–frequency (TF) images by a continuous wavelet transform, and a dual-branch GAN model with a symmetric structure is constructed. One branch utilizes an auxiliary classification GAN (ACGAN) to process the spatial information of the TF images, while the other employs a GAN with a frequency generator and a frequency discriminator to handle the frequency information of the input images after a fast Fourier transform. Then, a shuffle attention (SA) module based on an attention mechanism is integrated into the proposed model to improve the network's expression ability and reduce the computational burden. Simultaneously, mean square error (MSE) is integrated into the loss functions of both generators to enhance the consistency of frequency information for the generated images. Additionally, a Wasserstein distance and gradient penalty are also incorporated into the losses of the two discriminators to prevent gradient vanishing and mode collapse. Under the supervision of the frequency WGAN-GP branch, an ACWGAN-GP can generate high-quality fault samples to balance the dataset. Finally, the balanced dataset is utilized to train the auxiliary classifier to achieve fault diagnosis. The effectiveness of the proposed method is validated by two rolling bearing datasets. When the imbalanced ratios of the four datasets are 0.5, 0.2, 0.1, and 0.05, respectively, their average classification accuracy reaches 99.35% on the CWRU bearing dataset. Meanwhile, the average classification accuracy reaches 96.62% on the MFS bearing dataset. [ABSTRACT FROM AUTHOR]

Aiming at the vibration signal characteristics of multi-channel rolling bearing complex faults containing various shock components, a rolling bearing complex fault diagnosis model based on spatiotemporal intrinsic mode decomposition (STIMD) method and fast spectral kurtosis method was proposed. The spatiotemporal intrinsic mode decomposition method combines the signal atomic decomposition method with the idea of signal blind source separation. Through the fast independent component analysis and the nonlinear matching pursuit method of the established overcomplete dictionary base, various fault mode components are separated. The initial phase function selected based on the high kurtosis fault frequency band obtained by the fast spectral kurtosis method can better fit the bearing fault frequency domain characteristics, so that the spatiotemporal intrinsic mode decomposition method can more accurately separate various impact components in the vibration signal. The simulation model of bearing compound fault was established and the data collected from fault diagnosis experiment platform were used to verify that the STIMD method was effective in solving the problem of rolling bearing compound fault diagnosis. By analyzing the kurtosis changes under different signal noise ratio (SNR) conditions and comparing the simulation results with the fast independent component analysis method, it shows that the kurtosis index decomposed by the proposed method is more able to prove the existence of faults under the condition of low SNR, that is, the impact is completely covered by noise. Therefore, a spatiotemporal intrinsic mode decomposition method with fast spectral kurtosis optimization can solve the problem of blind source separation in the field of composite faults of multi-channel rolling bearings and realize composite fault diagnosis. [ABSTRACT FROM AUTHOR]

Since bearing fault signal in complex running status is usually characterized as nonlinear and non-stationary, it is difficult to extract accurate affluent features and achieve effective fault identification via conventional signal processing tools. In this article, a rolling bearing fault diagnosis technique based on variational mode decomposition and weighted multidimensional feature entropy fusion is proposed to address this issue, which is mainly composed of three procedures. First, the original signal undergoes the variational model decomposition. Next, the signal features are extracted by weighted multidimensional feature entropy as the input of the diagnosis model. Finally, the classification is performed by a convolutional neural network. The method is applied in simulation and experimental analysis. The experimental results show that the proposed method, which demonstrates strong immunity to noise and robustness, can more effectively and adaptively extract the fault features of rolling bearings and achieve the goal of identifying the rolling bearing fault category and damage degree under variable operating conditions. Meanwhile, this approach exhibits superior accuracy and identification performance to some similar entropy-based hybrid approaches referred to in this article, with a promising prospect in industrial application. [ABSTRACT FROM AUTHOR]

In order to solve the dependence of convolutional neural networks (CNN) on large samples of training data, an intelligent fault diagnosis method based on spectral kurtosis (SK) and attention mechanism is proposed. Firstly, the SK algorithm is used to obtain two-dimensional fast kurtosis graphs from vibration signals, and the two-dimensional fast spectral kurtosis graphs are converted into one-dimensional kurtosis time-domain samples, which are used as the input of CNN. Then the channel attention module (CAM) is added to CNN, and the weight is increased in the channel domain to eliminate the interference of invalid features. The accuracy of fault identification can reach 99.8 % by applying the proposed method on the fault diagnosis experiment of rolling bearings. Compared with the traditional deep learning (DL) method, the proposed method not only has higher accuracy, but also has lower dependence on the number of samples. [ABSTRACT FROM AUTHOR]

Prognostic health management (PHM) is essential for the predictive maintenance of industrial systems, aiming to predict the remaining useful life (RUL) of system to ensure safe, reliable, and cost-effective operation of the machinery. This work proposes an innovative method for RUL prediction of bearings, by combining a health indicator (HI) proposed from the absolute cumulative modified multiscale permutation entropy (C-MMPE) feature with a deep learning long short-term memory (LSTM) model. The work also introduces a virtual health degree for bearings, using an exponential degradation pattern as the target function for the LSTM model output. Experimental validation showcases the effectiveness of proposed approach, achieving a high score value of 0.81 and demonstrating a lower mean absolute error value of 7.38 in RUL prediction for test bearings compared to conventional features and regression labeling functions. This highlights the superior RUL prediction capability of the proposed methodology. [ABSTRACT FROM AUTHOR]

To solve the problems of insufficient samples and weak fault features of audio signals in the fault diagnosis of plunger pump, this paper proposes a fault diagnosis method of plunger pump based on audio signal combined with meta-transfer learning (MTL-PAFD). The method takes the audio signals of the plunger pump as samples, which are acquired by a single sensor. Through the Gammatone filter bank processing, the representation ability of the audio signal under strong noise interference is effectively improved. Then combined with meta-transfer learning, the few-shot fault diagnosis of plunger pump is realized. In addition, according to the actual needs of fault diagnosis of plunger pump, the test method of meta-transfer learning in fault diagnosis application is improved, which can process unknown fault classes adaptively. Experimental results show that MTL-PAFD has a fault diagnosis accuracy of 91.41% for seen classes. After fast adaptive learning, it can achieve an accuracy of 89.64% when identifying unseen fault classes. [ABSTRACT FROM AUTHOR]

The health of rolling bearing is of great importance for the normal operation of rotating machinery. The fault diagnosis process can be roughly summarized as signal processing, feature extraction, and fault classification. In this paper, a novel feature extraction and fault diagnosis method with fractional order back-propagation neural network is put forward. The new sine cosine algorithm optimized variational mode decomposition is performed on vibration signals, and the fault feature vectors are selected and built by singular value decomposition. Inspired by the fractional order calculus, a fractional order back-propagation neural network is employed to realize fault classification. The capability of the developed fault diagnosis algorithm is comprehensively evaluated via benchmark bearing data. The experimental results demonstrate that the designed method substantially extracts bearing defect features, increases classification accuracy and efficiency, and also outperforms existing algorithms. [ABSTRACT FROM AUTHOR]

The raceway damage of the bearing restricts the operation stability of the system. Therefore, in the case of damage, simulating vibration response and predicting the performance of the shaft-cylindrical roller-bearing-pedestal system have high research value. According to the structural characteristics and the force transfer relationship, a dynamic model of the shaft-cylindrical roller-bearing-pedestal system is established in this paper. Then, the vibration response of the system is simulated by Runge–Kutta algorithm and measured by experiment. Finally, the effects of load and speed, damage size, and radial clearance on the dynamic performance are investigated. The simulation and measurement results indicate that the load F and the speed N can promote the enhancement of the vibration acceleration signal. As the damage length continues to increase, the ring in the Poincaré map begins to break, and the system transits from quasi-periodic to chaotic motion. The vibration signal is more affected by the damage size than by the damage location. When the radial clearance increases from 5 × 10−6 m to 15 × 10−6 m, the PTP value increases from 23.83 m·s−2 to 61.36 m·s−2. [ABSTRACT FROM AUTHOR]

The rolling bearing fault signals are accompanied with the specific periodic impulse components in case of fault. Due to the background noise, the periodic impulse component is extremely weak and difficult to be extracted. The periodic segment matrix (PSM) has excellent singular value aggregation property and can accurately separate the periodic impulse components from the signal. Based on this, a symplectic period mode decomposition (SPMD) method is proposed. Firstly, the singular value squared difference ratio (SVSDR) spectrum is defined, and the embedded dimension of PSM is determined. Then, the symplectic geometry component matrix is obtained by symplectic geometry similarity transformation, and the first symplectic geometry period component (SPC) is obtained by periodic impact intensity (PII). Finally, the spectral L2/L1 norm (SNL2/L1) index is used as the termination condition, and the iterative decomposition is terminated when the SNL2/L1 of the residual signal decreases. The emulation and experimental signals results indicate that the SPMD method can extract periodic impulse components accurately and is an effective rolling bearing fault diagnosis method. [ABSTRACT FROM AUTHOR]

Small and imbalanced fault samples have a profound impact on the diagnostic performance of a model in the process of locating and quantifying the rolling bearing damage of aeroengines in practice. Therefore, a Siamese Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model was proposed in this paper. Random selection and cross combination methods were used to augment and balance sample sizes at first. Then, two weight-sharing CNN-BiLSTM models were used for adaptive extraction and distance measurement of weak fault features. Finally, the fault classification was performed based on feature distance. Model performance was verified using simulated fault test data of rolling bearings. The results showed that the Siamese CNN-BiLSTM model could achieve an accuracy of up to 96.0% for quantitative diagnosis and 98.0% for location diagnosis. This model was also capable of solving the imbalanced classification of samples and made it possible to transfer between different rotating speeds and working conditions. [ABSTRACT FROM AUTHOR]

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

Synchrosqueezed transform (SST) is a time–frequency analysis method that can improve energy aggregation and reconstruct signals, which has been applied in the fields of medical treatment, fault diagnosis, and seismic wave processing. However, when dealing with time-varying signals, SST suffers from poor time–frequency resolution and is unable to deal with long signals. In order to accurately extract the characteristic frequency of variable speed rolling bearing faults, this paper proposes a synchrosqueezed transform method based on fast kurtogram and demodulation and piecewise aggregate approximation (PAA). The method firstly filters and demodulates the original signal using fast kurtogram and Hilbert transform to reduce the influence of background noise and improve the time–frequency resolution. Then, it compresses the signal by using piecewise aggregate approximation, so that the SST can deal with long signals and, thus, extract the fault characteristic frequency. The experimental data verification results indicate that the method can effectively identify the fault characteristic frequency of variable-speed rolling bearings. [ABSTRACT FROM AUTHOR]

Rotary machines commonly use rolling element bearings to support rotation of the shafts. Most machine performance imperfections are related to bearing defects. Thus, reliable bearing condition monitoring systems are critically needed in industries to provide early warning of bearing fault so as to prevent machine performance degradation and reduce maintenance costs. The objective of this paper is to develop a smart monitoring system for real-time bearing fault detection and diagnostics. Firstly, a smart sensor-based data acquisition (DAQ) system is developed for wireless vibration signal collection. Secondly, a modified variational mode decomposition (MVMD) technique is proposed for nonstationary signal analysis and bearing fault detection. The proposed MVMD technique has several processing steps: (1) the signal is decomposed into a series of intrinsic mode functions (IMFs); (2) a correlation kurtosis method is suggested to choose the most representative IMFs and construct the analytical signal; (3) envelope spectrum analysis is performed to identify the representative features and to predict bearing fault. The effectiveness of the developed smart sensor DAQ system and the proposed MVMD technique is examined by systematic experimental tests. [ABSTRACT FROM AUTHOR]

The rolling bearing is one of the commonly used mechanical components in rotating machinery, and its health directly affects the normal operation of equipment. However, the fault signal of rolling bearing is susceptible to noise interference, which makes it difficult to extract the fault characteristics of the rolling bearing and thus affects the accuracy of the diagnosis results. To address this problem, this paper proposes a method by using a snake optimization algorithm to optimize variational mode decomposition (SOA-VMD) and applies it to the extraction of the fault feature of rolling bearing. First, the minimum Shannon entropy to kurtosis ratio (EKR) is used as the fitness function of SOA to search for the best parameter combination of VMD. Second, the optimized VMD is used to decompose the vibration signal of rolling bearing to obtain K intrinsic mode functions (IMFs). Then, the IMF with the most fault information is selected for reconstruction through EKR. The Teager–Kaiser energy operator (TKEO) spectrum analysis is performed on the reconstructed signal. Finally, this method is used to analyze the simulation signal and rolling bearing vibration signal and compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithms to verify the feasibility and effectiveness of the SOA-VMD method. [ABSTRACT FROM AUTHOR]

Traditional approaches to the intelligent fault diagnosis of rolling bearings have predominantly relied on manual expertise for feature extraction, a practice that compromises robustness. In addition, the existing convolutional neural network (CNN) is characterized by an overabundance of parameters and a substantial requirement for training samples. To address these limitations, this study introduces a novel fault diagnosis algorithm for rolling bearings, integrating a one-dimensional convolutional neural network (1DCNN) with a support vector machine (SVM) to form an enhanced 1DCNN-SVM model. This model is further refined using the sparrow search algorithm (SSA) for the optimal adjustment of the parameters of 1DCNN-SVM. Specifically, by substituting the CNN's final softmax layer with an SVM, the model becomes better suited for processing limited data volumes. In addition, the incorporation of batch normalization and dropout layers within the CNN framework significantly augments its fault classification accuracy for rolling bearings, concurrently mitigating the risk of overfitting. The SSA is subsequently applied to refine three principal hyper-parameters: batch size, initial learning rate, and the L2 regularization coefficient, thereby overcoming the challenges associated with manually adjusting parameters, such as extended processing times and unpredictable outcomes. Empirical tests on Case Western Reserve University (CWRU) datasets revealed the model's superior performance, with the SSA-optimized 1DCNN-SVM showcasing diagnostic accuracies over 98%, marked improvements over conventional models, and a significant reduction in processing times. This method not only marks a significant advancement in intelligent fault diagnosis for rolling bearings but also demonstrates the potential of integrating machine learning for more precise and efficient diagnostics. The SSA-1DCNN-SVM model, optimized for accuracy and minimal data use, sets a new standard in fault diagnosis, relevant for machinery health monitoring and maintenance strategies across various industries. [ABSTRACT FROM AUTHOR]

The vibration of the leveling mill is common in metallurgical enterprises, which causes vibration marks on the surface of the support rolls and affects the surface quality of the strip. This paper establishes a bearing-rotor system model for a leveling mill, and finds that reducing the bearing clearance helps to improve the stability of the mill. The study of different speeds under the amplitude of the change, the work of the roll system gap changes on the vibration of the mill. Finally, the results of the study can provide reference for the theory of vibration suppression of rolling mills. [ABSTRACT FROM AUTHOR]

Traditional fault detection methods in acoustic signal feature extraction of rolling bearings often make the signal denoising process complex due to low signal-to-noise ratio and weak fault features, making this method difficult to meet real-time requirements. Therefore, a fault detection model based on Fast-Renoriented SIFT feature extraction is proposed, which can quickly extract a large number of features from the original signal without the need for noise reduction processing and can effectively improve the efficiency and accuracy of fault detection. At the same time, to adapt to the fault detection of rolling bearings under multiple working conditions, this study also proposes an adaptive extended word bag model that combines local kurtosis and local 2-dimensional information entropy features, improving the adaptability and flexibility of the new model. It obtained a 100% overall recognition rate and a fault detection time of no more than 0.5 seconds in a 5-fold cross-validation experiment, verifying the excellent recognition accuracy, stability, and operational efficiency of the detection model. Its recognition accuracy in the multi-working condition rolling bearing fault detection experiment was above 97%, which was improved by about 21.25% compared to the traditional word bag model and had significant advantages in fault recognition accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

Roller bearing degradation features fractal characteristics such as self-similarity and long-range dependence (LRD). However, the existing remaining useful life (RUL) prediction models are memoryless or short-range dependent. To this end, we propose a RUL prediction model based on fractional Brownian motion (FBM). Bearing faults can happen in different places, and thus their degradation features are difficult to extract accurately. Through variational mode decomposition (VMD), the original degradation feature is decomposed into several components of different frequencies. The monotonicity, robustness and trends of the different components are calculated. The frequency component with the best metric values is selected as the training data. In this way, the performance of the prediction model is hugely improved. The unknown parameters in the degradation model are estimated by the maximum likelihood algorithm. The Monte Carlo method is applied to predict the RUL. A case study of a bearing is presented and the prediction performance is evaluated using multiple indicators. [ABSTRACT FROM AUTHOR]

Although intelligent fault diagnosis has achieved good results, the application in practical engineering scenarios is still unsatisfactory due to the lack of sufficient fault signals to support the training of the diagnosis methods and the difficulty of extracting sensitive fault features from the original signals. To address the problem that small-sample fault data limit the diagnostic performance of traditional neural networks, a multi-scale residual parametric convolutional capsule network (MRCCCN) for small-sample bearing fault diagnosis is proposed. In the MRCCCN, the input fault information is averaged and segmented multiple times and then the initial features of the multi-segmented input are extracted by residual parameterised convolution. Then, the multi-branch features are fused and fed into an improved parametric capsule network to further extract fault features and store feature information using dynamic routing. The performance of the MRCCCN is validated using the Case Western Reserve University (CWRU) rolling bearing dataset and the Paderborn University rolling bearing dataset of vibration signals and compared with some advanced deep learning methods. The comparison results show that the proposed MRCCCN is able to accurately diagnose faults under small-sample conditions and still has significant diagnostic performance in small-sample variable noise tests. [ABSTRACT FROM AUTHOR]

With the rapid development of smart manufacturing, data-driven deep learning (DL) methods are widely used for bearing fault diagnosis. Aiming at the problem of model training crashes when data are imbalanced and the difficulty of traditional signal analysis methods in effectively extracting fault features, this paper proposes an intelligent fault diagnosis method of rolling bearings based on Gramian Angular Difference Field (GADF) and Improved Dual Attention Residual Network (IDARN). The original vibration signals are encoded as 2D-GADF feature images for network input; the residual structures will incorporate dual attention mechanism to enhance the integration ability of the features, while the group normalization (GN) method is introduced to overcome the bias caused by data discrepancies; and then the model is trained to complete the classification of faults. In order to verify the superiority of the proposed method, the data obtained from Case Western Reserve University (CWRU) bearing data and bearing fault experimental equipment were compared with other popular DL methods, and the proposed model performed optimally. The method eventually achieved an average identification accuracy of 99.2% and 97.9% on two different types of datasets, respectively. [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]

Deep transfer learning has been widely used to improve the versatility of models. In the problem of cross-domain fault diagnosis in rolling bearings, most models require that the given data have a similar distribution, which limits the diagnostic effect and generalization of the model. This paper proposes a deep reconstruction transfer convolutional neural network (DRTCNN), which satisfies the domain adaptability of the model under cross-domain conditions. Firstly, the model uses a deep reconstruction convolutional automatic encoder for feature extraction and data reconstruction. Through sharing parameters and unsupervised training, the structural information of target domain samples is effectively used to extract domain-invariant features. Secondly, a new subdomain alignment loss function is introduced to align the subdomain distribution of the source domain and the target domain, which can improve the classification accuracy by reducing the intra-class distance and increasing the inter-class distance. In addition, a label smoothing algorithm considering the credibility of the sample is introduced to train the model classifier to avoid the impact of wrong labels on the training process. Three datasets are used to verify the versatility of the model, and the results show that the model has a high accuracy and stability. [ABSTRACT FROM AUTHOR]

Summary: Since bearing defects usually occur which threaten the stable operation of the machinery, bearing fault detection is of great importance. However, the bearing fault signals inevitably exhibit strong interference components due to the complex structures of the real equipment, which leads to difficulty in fault feature detection. To address the problem, a fault diagnosis method based on the ensemble average of enhanced generalized envelope spectrum (EAEGES) was constructed. First, to suppress the irrelevant components, the optimal analysis frequency band was determined based on the variable frequency band division criteria. Second, the optimal signal was divided into numerous sub‐signals, the EAEGES was established based on the principles of the generalized envelope spectrum to strengthen the capability in detecting bearing fault features, while the improved pulse extraction operator was utilized as the target. Finally, abundant fault information can be distinguished in the feature map acquired using the proposed technique. This technique shows high effectiveness in extracting the defect signatures of the rolling element bearing, which is demonstrated using the simulation and experimental signals and can be applied to real applications. [ABSTRACT FROM AUTHOR]

Intelligent fault diagnosis (IFD) based on deep learning methods has shown excellent performance, however, the fact that their implementation requires massive amount of data and lack of sufficient labeled data, limits their real-world application. In this paper, we propose a two-step technique to extract fault discriminative features using unlabeled and a limited number of labeled samples for classification. To this end, we first train an Autoencoder (AE) using unlabeled samples to extract a set of potentially useful features for classification purpose and consecutively, a Contrastive Learning-based post-training is applied to make use of limited available labeled samples to improve the feature set discriminability. Our Experiments—on SEU bearing dataset—show that unsupervised feature learning using AEs improves classification performance. In addition, we demonstrate the effectiveness of the employment of contrastive learning to perform the post-training process; this strategy outperforms Cross-Entropy based post-training in limited labeled information cases. [ABSTRACT FROM AUTHOR]

Repetitive transients are a key symptom for the occurrence of incipient fault of rolling element bearings. Therefore, an optimal wavelet filtering method is developed by maximizing the L-kurtosis through the genetic algorithm to extract the weak repetitive transients buried in the heavy noise and disturbed by the outliers. First, the capability of L-kurtosis for characterizing the impulsiveness and cyclostationary of repetitive transients is numerically studied at different degrees of noise. Then, the center frequency and band width of morlet wave filter are adaptively determined by the genetic algorithm and the maximization of L-kurtosis. Finally, both simulation and experiments are performed to validate the efficacy of the proposed method. Results show that the proposed method is more powerful and reliable than the other commonly used indexes-based optimal wavelet filtering methods. [ABSTRACT FROM AUTHOR]

• A new design of an oil-free rotary-vane compressor with a mechanical efficiency exceeding 0.9 has been proposed. • A mathematical model for calculating power losses due to friction for the new design of an oil-free rotary-vane compressor has been developed, with a calculation error not exceeding 10 %. • The power losses due to friction in the working chamber of the new oil-free rotary-vane compressor in the range of rotor speeds from 1000 to 3700 rpm are 4 to 20 times lower than those for the traditional design. Additionally, the frictional power losses in the rotor slot are 20–30 % lower, and on the stator, they are 16–20 times lower. This paper presents the design of an oil-free rotor vane stage with a new tribological scheme. The new scheme differs from existing ones in that the sealing vane contain protrusions that rest on rolling unloading bearings located in the end covers of the cylinder. Experimental results on friction power losses in the new and traditional stages are provided, demonstrating that the implementation of the new stage design allows for an average reduction in friction power losses by a factor of 10. A mathematical model for calculating friction power losses in the rotor vane type compressor stage, implementing the new tribological scheme, has been developed. The analysis based on the mathematical model confirms the possibility of achieving mechanical efficiency values higher than 0.9 in oil-free rotor vane stages with a pressure ratio of 3 and a rotor speed of 3000 rpm, through the use of the new tribological scheme. It has been found that there is a redistribution of the importance and values of the main design parameters recommended for the design of oil-free rotor vane stages with the new tribological scheme, which determine its structural configuration, compared to its traditional design. [ABSTRACT FROM AUTHOR]

Aiming at the problem of fault diagnosis and classification of rolling bearing and gear of gearboxes, a novel method based on matrix distance features of Gramian angular field (GAF) image is proposed based on sliding window compressible GAF transformation. The method converts the one-dimensional fault signal into a two-dimensional feature matrix and constructs the discrimination matrix of each fault category by establishing the mean value of the feature matrix of a priori samples. For the new sampled signal, after converting it into a two-dimensional feature matrix, the feature matrix is obtained. The fault classification is carried out by using the matrix distance between feature matrix and the discrimination matrix of each category. The method is validated by the test data of Case Western Reserve University and the acoustic emission data from a gearbox test bench. The classification accuracy is 99.17% and 95.71%, which presented the feasibility and effectiveness of the novel method proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes a rolling bearing fault diagnosis method based on the enhanced channel attention network. The vibration signals are collected via a wireless sensor node, which are input to the neural network, and channel attention block is used to strengthen the assignment of important features, so that the attention of network is paid onto the critical fault information. Furthermore, channel attention block and residual convolution block are combined to form an enhanced channel attention network to extract the detail features. Experiment results show that the model can achieve 100% recognition accuracy for rolling bearings in various working conditions. It is also shown that the proposed new learning algorithm can provide a higher diagnosis accuracy than those state-of-the-arts in a strong disturbance environment, which reflects better robustness and generalisation ability. [ABSTRACT FROM AUTHOR]

Rolling element bearings are widely used in aero engines, high-speed trains, precision machine tools, and other technical fields due to their high rotational accuracy and strong carrying capacity. With the development of rotating machinery in the direction of high speed, the working conditions of rolling element bearings are becoming more and more harsh, resulting in thermal problems that will seriously affect the mechanical properties of the bearing and the stability of the rotor system. In this paper, the four-degrees-of-freedom quasi-static model of cylindrical roller bearings was established based on the elastohydrodynamic lubrication theory combined with slicing technology. The local thermal approach and the thermal resistance network method were adopted to calculate the frictional heat generation and the transient temperature field of bearings. So the thermal-mechanical coupling model was obtained considering the correction of the thermal effect. On this basis, the influences of temperature on the vibration of the bearing-rotor system were researched. Finally, the temperature and stiffness of the bearings under various operating conditions were discussed. The coupling theory of thermal characteristics and mechanical properties of such bearing-rotor systems was improved. Practical Applications: Cylindrical roller bearings generally play a supporting role in rotating machinery and are widely used on high-speed occasions. For example, aero engines work under high-speed conditions, and high speeds in mechanical systems often bring serious thermal problems. As one of the core components, the spindle bearing must be able to overcome such a complex working environment. In this paper, cylindrical roller bearings and bearing-rotor systems are mathematically modeled for this feature in order to study their thermal and dynamic characteristics. The conclusions of this paper can provide relevant references for the design theory of this type of bearings applied in the fields of aero engines, high-speed precision machine tools, high-speed trains, etc., as well as theoretical guidance for the evaluation of their actual service performance. [ABSTRACT FROM AUTHOR]

Rolling bearing feature extraction and fault identification techniques using deep learning algorithms have been widely adopted in recent years. We proposed a method for diagnosing composite faults in rolling bearings by employing multisensor decision fusion and convolutional neural networks. Different types of bearing faults and eccentricity faults have different fault eigenfrequencies in vibration signals. In the proposed method, vibration and acoustic signals are collected, their characteristics are analyzed, and multisensor data fusion processing is conducted. A neural network is then used to identify the signals containing bearing fault characteristics to diagnose bearing faults at different rotational speeds. We demonstrated the effectiveness of the proposed method by conducting comparative experiments on existing methods. [ABSTRACT FROM AUTHOR]

Compared to fault diagnosis across operating conditions, the differences in data distribution between devices are more pronounced and better aligned with practical application needs. However, current research on transfer learning inadequately addresses fault diagnosis issues across devices. To better balance the relationship between computational resources and diagnostic accuracy, a knowledge distillation-based lightweight transfer learning framework for rolling bearing diagnosis is proposed in this study. Specifically, a deep teacher–student model based on variable-scale residual networks is constructed to learn domain-invariant features relevant to fault classification within both the source and target domain data. Subsequently, a knowledge distillation framework incorporating a temperature factor is established to transfer fault features learned by the large teacher model in the source domain to the smaller student model, thereby reducing computational and parameter overhead. Finally, a multi-kernel domain adaptation method is employed to capture the feature probability distribution distance of fault characteristics between the source and target domains in Reproducing Kernel Hilbert Space (RKHS), and domain-invariant features are learned by minimizing the distribution distance between them. The effectiveness and applicability of the proposed method in situations of incomplete data across device types were validated through two engineering cases, spanning device models and transitioning from laboratory equipment to real-world operational devices. [ABSTRACT FROM AUTHOR]

The current methods for early fault diagnosis of rolling bearing have some flaws, such as poor fault feature information and insufficient fault feature extraction capability, which makes it challenging to guarantee fault diagnosis accuracy. In order to increase the accuracy of fault diagnosis, it proposes a new fault diagnosis method based on enhanced Symplectic geometry mode decomposition with cosine difference factor and calculus operator (ESGMD-CC) and bat algorithm (BA) optimized extreme learning machine (ELM). The vibration signal is first decomposed into a number of Symplectic geometry components (SGCs) by SGMD. The number of iterations is reduced by the cosine difference factor, which also successfully separates the noise components from the effective components. The calculus operator is adopted to strengthen the weak fault features, making it simple to extract. The fault feature vectors are calculated by the power spectrum entropy-weighted singular values. Finally, the ELM model optimized by BA iteratively is performed as the final classifier for fault classification. The simulation and experiments demonstrate that the proposed method has a better degree of fault diagnostic accuracy and is effective at extracting the rich fault information from vibration signals. [ABSTRACT FROM AUTHOR]

Rolling bearing feature extraction and fault identification techniques using deep learning algorithms have been widely adopted in recent years. We proposed a method for diagnosing composite faults in rolling bearings by employing multisensor decision fusion and convolutional neural networks. Different types of bearing faults and eccentricity faults have different fault eigenfrequencies in vibration signals. In the proposed method, vibration and acoustic signals are collected, their characteristics are analyzed, and multisensor data fusion processing is conducted. A neural network is then used to identify the signals containing bearing fault characteristics to diagnose bearing faults at different rotational speeds. We demonstrated the effectiveness of the proposed method by conducting comparative experiments on existing methods. [ABSTRACT FROM AUTHOR]

Aiming to address the problem that faults in rolling bearings make effective fault diagnosis difficult under small-sample and varying working conditions, this paper proposes a new fault diagnosis method for rolling bearings that monitors their vibration signals and is based on an improved deep residual Siamese neural network, called a WDRCNN. Firstly, the Siamese neural network is applied to extract features with shared weights to achieve an expansion in the number of fault samples. Then, multiple residual blocks are used to extract deeper feature information and effectively alleviate the problem of overfitting. In addition, the attention mechanism is employed to assign weights to the feature information to reduce the interference of redundant features. Finally, the Euclidean distance between the sample pairs is calculated to determine the similarity of the sample pairs and to classify bearing faults for end-to-end bearing fault diagnosis. The experimental results demonstrate that the WDRCNN achieves an average accuracy of 96.31% under different operating conditions. Even when only 90 training samples are available, the WDRCNN achieves an accuracy of over 93%. [ABSTRACT FROM AUTHOR]

Aiming at the difficult problem of extracting fault characteristics and the low accuracy of fault diagnosis throughout the full life cycle of rolling bearings, a fault diagnosis method for rolling bearings based on grey relation degree is proposed in this paper. Firstly, the subtraction-average-based optimizer is used to optimize the parameters of the variational mode decomposition algorithm. Secondly, the vibration signals of bearings are decomposed by using the optimized results, and the feature vector of the intrinsic mode function component corresponding to the minimum envelope entropy is extracted. Finally, the grey proximity and similarity relation degree based on standard distance entropy are weighted to calculate the grey comprehensive relation degree between the feature vector of vibration signals and each standard state. By comparing the results, the diagnosis of different fault states and degrees of rolling bearings is realized. The XJTU-SY dataset was used for experimentation, and the results show that the proposed method achieves a diagnostic accuracy of 95.24% and has better diagnosis performance compared to various algorithms. It provides a reference for the fault diagnosis of rolling bearings throughout the full life cycle. [ABSTRACT FROM AUTHOR]

The vibration signal of rolling bearing is interfered by the coupling of other various components and environment, which brings challenges to effective feature expression and the establishment of accurate predictor. A fault diagnosis method via 2D feature map of cyclic spectral coherence (CSCoh) after denoising and multi-scale convolutional neural network (MSCNN) under different conditions is proposed. The original vibration signal is processed by DTCWPT and threshold denoising, and a 2D feature map extraction model based on CSCoh is established to reflect hidden periodic characteristics. The feature differences can be highlighted and the coupling interference can be further eliminated. The multi-scale convolution kernels are proposed to build a parallel structure, and a fusion structure is followed. Benefit by the structures in MSCNN, the local and global features of 2D feature map can be preserved comprehensively, the learning ability and diagnostic performance can be improved. Finally, the verification experiments of rolling bearings in different positions under different operating conditions are carried out, and the experimental results show the proposed method has better generalization ability and comprehensive accuracy of more than 95% under different conditions. [ABSTRACT FROM AUTHOR]

Compound faults identification from vibration signals is still a challenge for rotating machinery because the multiple periodic impulsive fault signals may oscillate with the same frequency. In this research, a method termed enhanced two-layer sliding correlated kurtosis (TLSCK) is presented for isolating and identifying the compound defects from the monitored data containing strong background noise and compound faults. The method contains two main steps: the dual-tree complex wavelet package transform (DTCWPT) based correlated kurtogram is conducted on the raw monitored data as a frequency filtering step; further, the enhanced TLSCK method is conducted to diagnosis the compound defects from above filtered signals. The output signal of the enhanced TLSCK could locate the occurrence of interested fault impulses, while the unwanted vibration components and residual noise are well eliminated. Both numerically simulated and experimentally measured vibration data of damaged rolling bearings are analyzed via the presented method to test its performance, and the analysis results validate that the proposed method is effective in detecting compound faults of rotating machinery. [ABSTRACT FROM AUTHOR]

Deep learning has been used to enhance the efficiency of rolling bearing fault diagnosis. However, the complexity of working conditions in rolling bearings, coupled with varying data distribution, often results in the failure of most deep learning models. To solve this problem, a feature extractor with an attention mechanism is constructed, which allows the model to selectively study and preserve critical features relevant to fault information during the training process. Moreover, the Wasserstein distance is employed in adversarial neural networks to calculate the distribution discrepancy between data from different domains, which can avoid disappearing gradients or vanishing problems of the model. The experimental validation are conducted using the rolling bearing datasets from CWRU and Jiangnan University (JNU). Compared with other existing models, the proposed model has better performance under variable conditions, and this demonstrates the superiority and accuracy of this model. [ABSTRACT FROM AUTHOR]

Rolling bearings are essential parts in machine equipment and detecting damage in the early stage is crucial for ensuring the safe production and machine life. However, it is difficult to extract weak fault features under strong background noise, discrete harmonic frequency interference and non‐stationary service conditions. This investigation proposes a hybrid fault diagnosis approach utilizing transient structure‐optimal variational mode decomposition (TS‐OVMD) and adaptive group sparse coding (AGSC) for addressing the formidable problem. According to the singular value structure between transient signal and the interference signal, this work investigates the singular value shrinkage (SVS) technique to adaptively obtain the independent components number. Then, we present a transient structure measure (TSM) to adaptively optimize the balance factor. This measure index systematically quantifies the typical characteristics of the bearing fault signal, which can maximize the fault information representation and effectively reduces the useful information loss caused by improper selection of VMD parameters. Finally, a sparse coding model called AGSC is furthermore designed to enhance the fault impulses readability and suppress residual noise based on the sparsity within group property and the TSM. The proposed approach is verified using experimental data and is found to be superiority comparison with the state‐of‐the‐art method. [ABSTRACT FROM AUTHOR]

To address the challenges faced in the prediction of rolling bearing life, where temporal signals are affected by noise, making fault feature extraction difficult and resulting in low prediction accuracy, a method based on optimal time–frequency spectra and the DenseNet-ALSTM network is proposed. Firstly, a signal reconstruction method is introduced to enhance vibration signals. This involves using the CEEMDAN deconvolution method combined with the Teager energy operator for signal reconstruction, aiming to denoise the signals and highlight fault impacts. Subsequently, a method based on the snake optimizer (SO) is proposed to optimize the generalized S-transform (GST) time–frequency spectra of the enhanced signals, obtaining the optimal time–frequency spectra. Finally, all sample data are transformed into the optimal time–frequency spectrum set and input into the DenseNet-ALSTM network for life prediction. The comparison experiment and ablation experiment show that the proposed method has high prediction accuracy and ideal prediction performance. The optimization terms used in different contexts in this paper are due to different optimization methods, specifically the CEEMDAN method. [ABSTRACT FROM AUTHOR]

Intelligent motor fault diagnosis in industrial applications requires identifying key characteristics to differentiate various fault types effectively. Solely relying on statistical features cannot guarantee high classification accuracy, while complex feature extraction techniques can pose challenges for industry practitioners. Conversely, advanced feature extraction may not ensure that the model effectively learns these features for classification. A feature fusion approach that combines statistical and deep learning features to address these challenges is proposed. Since statistical features form the foundation for general feature extraction, statistical and deep learning features are combined using Extreme Gradient Boosting (XGBoost) algorithm with Particle Swarm Optimization (PSO). The PSO algorithm automates parameter tuning for XGBoost. A deep neural network (DNN) adaptively extracts hidden features, improving bearing fault classification precision using t‐SNE representation. Results successfully prove the DNN's ability to classify diverse motor faults using deep learning features. Thus, integrating statistical features with XGBoost further enhances DNN's performance. To ensure robustness, the proposed method has been compared with different motor fault classification methods and validated across different motor fault datasets, showcasing improved classification accuracy and robust performance, even amidst varying noise levels. This approach represents a promising advancement in intelligent fault diagnosis within industrial contexts. [ABSTRACT FROM AUTHOR]

In response to the high-noise, nonlinear, and nonstationary characteristics of vibration signals from aircraft environmental control system (ECS) turbofan rolling bearings, this paper proposes a diagnostic method for the degree of ECS turbofan bearing faults based on the Hidden Markov Model (HMM). Experimental results demonstrate that HMM can accurately diagnose and predict faults in ECS turbofan rolling bearings. The HMM method enhances diagnostic accuracy, and its effectiveness and feasibility in fault diagnosis based on different rolling bearing fault instances are elaborated. By employing the HMM model to establish precise models from decomposed dynamic data, it successfully identifies faults such as the fracture of the bearing cage under biased load conditions, although its performance in recognizing overheating faults is suboptimal. [ABSTRACT FROM AUTHOR]