Bearing Fault Research Articles

Research on a Bearing Fault Diagnosis Method Based on an Improved Wasserstein Generative Adversarial Network

In this paper, an advanced Wasserstein generative adversarial network (WGAN)-based bearing fault diagnosis approach is proposed to bolster the diagnostic efficacy of conventional WGANs and tackle the challenge of selecting optimal hyperparameters while reducing the reliance on sample labeling. Raw vibration signals undergo continuous wavelet transform (CWT) processing to generate time–frequency images that align with the model’s input dimensions. Subsequently, these images are incorporated into a region-based fully convolutional network (R-FCN), substituting the traditional discriminator for feature capturing. The WGAN model is refined through the utilization of the Bayesian optimization algorithm (BOA) to optimize the generator and discriminator’s semi-supervised learning loss function. This approach is verified using the Case Western Reserve University (CWRU) dataset and a centrifugal pump failure experimental dataset. The results showed improvements in data input generalization and fault feature extraction capabilities. By avoiding the need to label large quantities of sample data, the diagnostic accuracy was improved to 98.9% and 97.4%.

A High-Speed Train Axle Box Bearing Fault Diagnosis Method Based on Dimension Reduction Fusion and the Optimal Bandpass Filtering Demodulation Spectrum of Multi-Dimensional Signals

The operating state of axle box bearings is crucial to the safety of high-speed trains, and the vibration acceleration signal is a commonly used bearing-health-state monitoring signal. In order to extract hidden characteristic frequency information from the vibration acceleration signal of axle box bearings for fault diagnosis, a method for extracting the fault characteristic frequency based on principal component analysis (PCA) fusion and the optimal bandpass filtered denoising signal analytic energy operator (AEO) demodulation spectrum is proposed in this paper. PCA is used to measure the dimension reduction and fusion of three-direction vibration acceleration, reducing the interference of irrelevant noise components. A new type of multi-channel bandpass filter bank is constructed to obtain filtering signals in different frequency intervals. A new, improved average kurtosis index is used to select the optimal filtering signals for different channel filters in a bandpass filter bank. A dimensionless characteristic index characteristic frequency energy concentration coefficient (CFECC) is proposed for the first time to describe the energy prominence ability of characteristic frequency in the spectrum and can be used to determine the bearing fault type. The effectiveness and applicability of the proposed method are verified using the simulation signals and experimental signals of four fault bearing test cases. The results demonstrate the effectiveness of the proposed method for fault diagnosis and its advantages over other methods.

A Novel Signal Denoising Method Using an Analytical Signal-Based SVD and Its Applications in Bearing Fault Diagnosis

A Novel Signal Denoising Method Using an Analytical Signal-Based SVD and Its Applications in Bearing Fault Diagnosis

Bearing Faults Diagnosis by Current Envelope Analysis under Direct Torque Control Based on Neural Networks and Fuzzy Logic—A Comparative Study

Diagnosing bearing defects (BFs) in squirrel cage induction machines (SCIMs) is essential to ensure their proper functioning and avoid costly breakdowns. This paper presents an innovative approach that combines intelligent direct torque control (DTC) with the use of Hilbert transform (HT) to detect and classify these BFs. The intelligent DTC allows precise control of the electromagnetic torque of the asynchronous machine, thus providing a quick response to BFs. Using HT, stator current is analyzed to extract important features related to BFs. The HT provides the analytical signal of the current, thus facilitating the detection of anomalies associated with BFs. The approach presented incorporates an intelligent DTC that adapts to stator current variations and characteristics extracted via the HT. This intelligent control uses advanced algorithms such as neural networks (ANN-DTCs) and fuzzy logic (FL-DTCs). In this paper, a comparison between these two algorithms was performed in the MATLAB/Simulink environment for a three-phase asynchronous machine to evaluate their effectiveness under the proposed approach. The results obtained demonstrated a high ability to detect and classify BFs, confirming the effectiveness of each algorithm. In addition, this comparison highlighted the specific advantages and disadvantages of each approach. This information is valuable in choosing the most suitable algorithm according to the constraints and specific needs of the application.

Partial Transfer Learning Method Based on Inter-Class Feature Transfer for Rolling Bearing Fault Diagnosis.

Rolling bearing fault diagnosis methods based on transfer learning always assume that the sample classes in the target domain are consistent with those in the source domain during the training phase. However, it is difficult to collect all fault classes in the early stage of mechanical application. The more likely situation is that the training data in the target domain only contain a subset of the entire health state, which will lead to the problem of label imbalance compared with the source domain. The outlier classes in the source domain that do not have corresponding target domain samples for feature alignment will interfere with the feature transfer of other classes. To address this specific challenge, this study introduces an innovative inter-class feature transfer fault diagnosis approach. By leveraging label information, the method distinctively computes the distribution discrepancies among shared classes, thereby circumventing the deleterious influence of outlier classes on the transfer procedure. Empirical evaluations on two rolling bearing datasets, encompassing multiple partial transfer tasks, substantiate that the proposed method surpasses other approaches, offering a novel and efficacious solution for the realm of intelligent bearing fault diagnosis.

Rolling Bearing Fault Diagnosis in Agricultural Machinery Based on Multi-Source Locally Adaptive Graph Convolution

Maintaining agricultural machinery is crucial for efficient mechanized farming. Specifically, diagnosing faults in rolling bearings, which are essential rotating components, is of significant importance. Domain-adaptive technology often addresses the challenge of limited labeled data from a single source domain. However, information transfer can sometimes fall short in providing adequate relevant details for supporting target diagnosis tasks, leading to poor recognition performance. This paper introduces a novel fault diagnosis model based on a multi-source locally adaptive graph convolution network to diagnose rolling bearing faults in agricultural machinery. The model initially employs an overlapping sampling method to enhance sample data. Recognizing that two-dimensional time–frequency signals possess richer spatial characteristics in neural networks, wavelet transform is used to convert time series samples into time–frequency graph samples before feeding them into the feature network. This approach constructs a sample data pair from both source and target domains. Furthermore, a feature extraction network is developed by integrating the strengths of deep residual networks and graph convolutional networks, enabling the model to better learn invariant features across domains. The locally adaptive method aids the model in more effectively aligning features from the source and target domains. The model incorporates a Softmax layer as the bearing state classifier, which is set up after the graph convolutional network layer, and outputs bearing state recognition results upon reaching a set number of iterations. The proposed method’s effectiveness was validated using a bearing dataset from Jiangnan University. For three different groups of bearing fault diagnosis tasks under varying working conditions, the proposed method achieved recognition accuracies above 99%, with an improvement of 0.30%-4.33% compared to single-source domain diagnosis models. Comparative results indicate that the proposed method can effectively identify bearing states even without target domain labels, showcasing its practical engineering application value.

A multi-scale feature extraction and fusion method for diagnosing bearing faults

Bearing fault diagnosis is vital to safeguard the heath of rotating machinery. It can help to avoid economic losses and safe accidents in time. Effective feature extraction is the premise of diagnosing bearing faults. However, effective features characterizing the health status of bearings are difficult to extract from the raw bearing vibration signals. Furthermore, inefficient feature extraction results in substantial time wastage, making it hard to apply in real time monitoring. A novel feature extraction method for diagnosing bearing faults using multi-scale improved envelope spectrum entropy (MIESE) is proposed in this work. First, bearing vibration signals are analyzed across multiple scales, and improved envelope spectrum entropy (IESE) is extracted from these signals at each scale to form an original feature set. Subsequently, joint approximate diagonalization eigen (JADE) is applied to fuse above feature set for effectively eliminating redundancy and generated a refined feature set. Finally, the newly generated feature set is input into support vector machines (SVM) to effectively diagnose bearing health status. Two cases studies are employed to demonstrate the reliability of the proposed method. The results illustrate the proposed method can improve the stability of extracted features and increase the computational efficiency. Conflict of Interest Statement The authors declare no conflicts of interest.

An adaptive feature mode decomposition-guided phase space feature extraction method for rolling bearing fault diagnosis

Abstract The extraction of fault features from rolling bearings is a challenging and highly important task. Since they have complex operating conditions and are usually under a strong noise background. In this study, a novel approach termed phase space feature extraction guided by an adaptive feature mode decomposition (AFMDPSFE) is proposed to detect subtle faults in rolling bearings. Initially, a new method using Kullback-Leiber divergence is introduced to automatically select the optimal mode number and filter length for the decomposition of vibration signals, facilitating the automatic extraction of optimal components and ensuring efficient screening. This eliminates the need for manual configuration of feature mode decomposition parameters. Furthermore, a criterion that could determine two crucial parameters to capture system dynamics characteristics in phase space reconstruction is embedded into AFMDPSFE algorithm. Subsequently, a series of high-dimensional independent components is derived. The envelope spectrum of the principal component exhibiting the highest kurtosis value is computed to achieve fault identification, consequently enhancing the separation of signal from noise. Both simulations and experimental results confirm the effectiveness of AFMDPSFE approach. A comparison analysis shows the excellent performance of AFMDPSFE in extracting fault features from significant noise interference.

Rolling Bearing Fault Diagnosis Based on SABO-VMD and WMH-KNN.

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.

Multi-fault bearing diagnosis under time-varying conditions using Empirical Wavelet Transform, Gaussian mixture model, and Random Forest classifier

Bearing faults can cause heavy disruptions in machinery operation, which is why their reliable diagnosis is crucial. While current research into bearing fault analysis focuses on analyzing vibration data under constant working conditions, it is important to consider the challenges that arise when machinery runs at variable speeds, which is usually the case. This article proposes a multistage classifier for diagnosing bearings under time-variable conditions. We validate our method using vibration signals from five bearing health states, including a combined fault case. Our approach involves decomposing the signals using Empirical Wavelet Transform and computing temporal and frequency domain attributes. We use the Expectation-Maximization Gaussian mixture model for optimization concerns to identify relevant parameters and train the Random Forest classifier with the selected features. Our method, evaluated using the Polygon Area Metric, has demonstrated high effectiveness in diagnosing bearings under time-variable conditions. Our approach offers a promising solution that efficiently addresses speed variability and combined fault recognition issues.

Rolling bearing fault detection method based on teager energy operator

Abstract This study presents a method for detecting subtle faults in rolling bearings by integrating singular value decomposition (SVD) and local modal decomposition (LMD). Initially, the original vibration signals undergo reconstruction in phase space and subsequent denoising using SVD. Following this, LMD is employed to generate multiple local modal functions (IMFs), with the most sensitive IMF components selected based on kurtosis-mean square deviation criteria. The Teager energy operator (TEO) is then computed for these selected IMFs. Ultimately, Fourier transform analysis of TEO is utilized to identify faults in the inner and outer races of rolling bearings. Test results conducted on publicly available data from Case Western Reserve University validate the accurate identification of inner and outer race faults using this approach.

Complete Ensemble Empirical Mode Decomposition with Adaptive Noise to Extract Deep Information of Bearing Fault in Steam Turbines via Deep Belief Network

Extracting to enhance the accuracy of diagnosing bearing faults in steam turbines, a novel approach focused on extracting key fault features from vibration signals is introduced. Recognizing the complex, non-linear, and non-stationary nature of bearing vibration signals, our strategy involves a sensitivity analysis utilizing a multivariate diagnostic algorithm. The process begins with collecting vibration data from defective bearings via the TMI system. This data is then subjected to Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), enabling the integration of adaptive noise for the extraction of in-depth information. Following this, an analysis in both time and frequency domains — post Fast Fourier Transform (FFT) — is conducted on the decomposed signals, forming the basis of a diagnostic features database. To streamline data analysis and boost the model’s computational efficiency, a combination of eXtreme Gradient Boosting (XGBoost) and Mutual Information Criterion (MIC) is applied for dimensionality reduction. Furthermore, a deep belief network (DBN) is implemented to develop a precise fault diagnosis model for the bearings in rotating machinery. By incorporating sensitivity analysis, a diagnostic matrix is crafted, facilitating highly accurate fault identification. The superiority of this diagnostic algorithm is corroborated by testing with real on-site data and a benchmark database, demonstrating its enhanced diagnostic capabilities relative to other feature selection techniques.

A novel simulation-assisted transfer method for bearing unknown fault diagnosis

Abstract Supervised data-driven bearing fault diagnosis methods rely on completed datasets of faults, which can be challenging for signals collected in real engineering. Recognizing unknown faults using a data-driven approach is particularly difficult, as purposefully modeling these faults is complex. To address this challenge, this study proposes a new simulation-assisted transfer bearing unknown fault diagnosis method for realizing unknown compound fault diagnosis of rotating machinery. Firstly, finite element method is used to obtain the compound fault data that does not exist in the historical data, and wavelet packet transform is performed on the simulated and measured signals to enhance the detailed features of the signals. Then, a deep convolutional feature fusion network based on hybrid multi-wavelet spatial attention is constructed to fuse the time-frequency information processed by different wavelet bases. Finally, by integrating the concepts of intra-class splitting and transfer learning, the model is fine-tuned using simulation data to recognize unknown compound faults of rolling bearings. The method validates the simulated signals’ feasibility and the unknown faults’ diagnostic validity under the publicly available rolling bearings dataset. Compared to the comparison methods, the method’s accuracy increased by 2.86%, 2.61%, 5.41%, 4.77%, and 7.07%, respectively.

Efficient vibration analysis system using empirical mode decomposition residual signal and multi-axis data

The empirical mode decomposition (EMD) method is a technique that recursively decomposes an input signal into intrinsic mode functions (IMFs) by residual signals, primarily for identifying desirable features. The suggested algorithm observes the residual signal instead of the IMF, which lowers the computing load. The study introduces a new method for detecting bearing faults by enhancing signal extraction from sensor data using EMD and multi-axis feature extraction. This method streamlines the process by filtering out high-frequency noise and correlating residual signal information with analysis. The approach also enhances the signal-to-noise ratio (SNR) and feature signature identification using digital signal processing (DSP) techniques. The algorithm for vibration data analysis is tested for bearing failures, identifying shaft frequency and inner race bearing faults, which can be implemented in parallel. For the inner race fault bearing analysis, two-level EMD with a residual signal generates output similar to five-iteration EMD, saving 60% of computations. The use of spectral multiplication to multi-axis data processing produced a rise in the SNR of 18.32 dB to 20.92 dB for Y-axis and X-axis input, respectively. When compared to the single-axis IMF data computation, 20% fewer iterations are needed overall. A single-level EMD is adequate for calculating the rotational frequency of a healthy bearing. For the Y- and X-axis input, multi-axis analysis increases SNR by 10.68 dB and 13.14 dB, accordingly. This comprehensive strategy reduces computational complexity, improves fault detection accuracy, and minimizes noise impact, making it a promising solution for bearing fault detection.

A new multi-layer adaptation cross-domain model for bearing fault diagnosis under different operating conditions

Abstract Bearing faults under different operating conditions often cannot be diagnosed by models trained under a single operational condition. Additionally, the extraction of domain-invariant features in domain adaptation (DA) algorithms is also a challenge. To address the aforementioned issues, a multi-layer adaptation model based on an improved sparse autoencoders (SAEs) and dual-domain distance mechanism (ISAE-DDM) is proposed. First, the feature extraction capability of traditional SAEs is enhanced by a strategy that combines mean squared error with mean absolute error. Subsequently, the features of data under multiple hidden layers are extracted by the ISAE. Then, the distribution discrepancy between the source domain and target domain are measured by a dual-domain distance approach that combines Wasserstein distance with multi-kernel maximum mean discrepancy. Then, the domain distance loss under each hidden layer is assigned different weighting parameters. Finally, a joint metric DA mechanism across multiple hidden layers is constructed to achieve a better domain alignment. The performance of the proposed method is demonstrated by two different bearing experiments. Moreover, this model exhibits higher stability, and generalization capabilities compared to other methods.

Intelligent fault diagnosis of bearings driven by double-level data fusion based on multichannel sample fusion and feature fusion under time-varying speed conditions

Bearing fault diagnosis holds paramount importance in guaranteeing the smooth operation of mechanical systems. However, single-channel vibration sensor data poses limitations in providing a comprehensive representation of bearing fault characteristics, and the fault diagnosis model extracts fault features with insufficient representativeness, resulting in low reliability and poor generalization capability of the model in the face of variable operating conditions. Therefore, a novel intelligent fault diagnosis model of bearings driven by double-level data fusion (DLDF) is developed in this paper. Initially, the fusion weights of each channel's data are obtained using the information entropy method. Then, a novel multi-modal image fusion strategy is proposed to integrate the time-frequency representations of vibration data from channels X, Y, and Z at a specific measurement point. Finally, the shallow and deep feature components extracted from the CNN model and the feature components obtained through different mapping methods are fused to extract more representative fault features, achieving a reliable diagnosis of bearing faults under time-varying speed conditions. The efficacy and reliability of the proposed approach are demonstrated through the fault diagnosis outcomes of two sets of bearing experimental data under time-varying speed conditions.

Fault Diagnosis Method of Bearings Based on SCSSA-VMD-MCKD

To tackle the issue of detecting early, subtle faults in rolling bearings in the presence of noise interference, the SCSSA-VMD-MCKD method is suggested. This method optimizes the Variational Mode Decomposition (VMD) and Maximum Correlated Kurtosis Deconvolution (MCKD) by integrating the sine-cosine and Cauchy Mutation Sparrow Search Algorithm (SCSSA). The approach aims to effectively diagnose faults in rolling bearings by leveraging the strengths of VMD and MCKD in noise reduction and highlighting fault frequencies. The method utilizes the SCSSA algorithm to autonomously search for parameters in both VMD and MCKD, using the EnvelopeCrest Factor Ec as a fitness function. Firstly, SCSSA is employed to optimize the decomposition mode number K and penalty factor α in the VMD algorithm. Secondly, the parameters in the MCKD algorithm are optimized, and MCKD is used for deconvolution to enhance the impulsive characteristics of the best modal component. Finally, the signal is further analyzed after deconvolution. The results demonstrate that this algorithm can effectively identify subtle fault signals in bearing signals and diagnose fault frequencies in noisy environments. The accuracy of fault diagnosis can reach nearly 99%.

Fault Diagnosis and Prediction of Remaining Useful Life (RUL) of Rolling Element Bearing : A review state of art

Fault diagnosis of rolling element bearings is a critical aspect of machine maintenance and reliability. Bearings are extensively used in various industrial applications, and their failure can lead to costly downtime and equipment damage. Rotating machinery under continuous overload conditions can indeed significantly degrade bearing life and lead to various other issues. To identify issues in rolling element bearings (REB), several techniques and methods are employed. Diagnosing faults in ball bearings while simultaneously estimating the Remaining Useful Life (RUL) of the bearing is a crucial aspect of predictive maintenance. This can be achieved through a combination of signal processing techniques, machine learning methods, and RUL prediction models. The estimation of a bearing Remaining Useful Life (RUL) is of significant importance in predictive maintenance strategies to avoid unexpected failures, reduce downtime, and optimize maintenance costs. This literature review aims to explore the methodologies, techniques, and advancements in predicting the remaining useful life of bearings.

Multi-rolling element faults diagnosis of rolling bearing based on time-frequency analysis and multi-curves extraction

Abstract In industrial production, rolling bearings are widely used as key mechanical components in all types of rotating machinery. Fault diagnosis is essential for predicting bearing damage in advance, avoiding sudden equipment downtime and reducing economic losses. However, rolling element fault diagnosis of rolling bearings continues to be a challenge, especially with multi-rolling element faults. In view of the characteristics of randomness, weakness, and coupling in the vibration signal generated by multi-rolling element faults in rolling bearings, a multi-rolling element fault detection method is proposed by combination time-frequency (TF) analysis (TFA) with multi-curves extraction methods. The pre-processing method combined autoregressive model with maximum correlated kurtosis deconvolution is employed to enhance the weak periodic fault impulses in the raw vibration signals of the rolling bearing. Then an improved dynamic path multi-curves extraction method is proposed to extract multiple TF curves from the TF spectrogram (TFS) constructed via short-time Fourier transform. According to the proposed classification criteria, the TF curves are classified as homologous faults. The TF masking (TFM) method is employed to keep TF information closely associated with the fault impulse. Finally, the fault signals are reconstructed sequentially based on the TFS processed by TFM, and precise identification of multi-rolling element faults is achieved by envelope analysis. Experimental results demonstrate the effectiveness of the proposed method in extracting the weak fault features of multi-rolling elements and accomplishing fault separation and diagnosis.

Real-time bearing fault classification of induction motor using enhanced inception ResNet-V2

ABSTRACT The rolling bearing is a vital part used in different rotating electrical devices. Detecting defects in bearings is crucial for the safe operation of these machines. However, it is challenging to use Deep Learning techniques to identify bearing defects when the machine is not under load. To resolve this issue, this paper presents the Constant-Q Non-stationary Gabor Transform with enhanced Inception ResNet-V2, proposed for the early-stage classification of ball bearing faults in induction motors. The proposed model obtained the vibration images, i.e. time-frequency images of unfiltered vibration signals from the laboratory experimental setup. These images are applied to the proposed model, which classifies the ball bearing faults under various load conditions while adjusting its hyperparameter values instead of employing default ones. Furthermore, the model underwent training using k-fold method to assess its resilience with the use of optimal values obtained from hyperparameter tuning. The model is evaluated by performance metrics like F1-score, Recall, Precision, Confusion Matrix and Training time. The proposed model accomplished an average classification accuracy of 99.84% in low load and full load conditions within a few epochs. Ultimately, when compared to Inception-V4 and ResNet-50, which achieved 91.41% and 91.65%, respectively, the experimental findings unambiguously demonstrate the superior performance of the proposed model over both models.