Bearing Fault Feature Research Articles

A time-varying instantaneous frequency fault features extraction method of rolling bearing under variable speed

Under variable speed, the time-frequency analysis (TFA) method for processing rolling bearing vibration signal is susceptible to noise, which cannot extract accurate time-frequency features. Plus, the special meaning of each time-frequency feature remains unclear. Based on the dynamic characteristics of rolling bearing and combined with the improved multisynchrosqueezing transform (IMSST), empirical Fourier decomposition (EFD), and generalized demodulation (GD), a time-varying instantaneous frequency fault features extraction method of rolling bearing under variable speed is proposed in this paper. Firstly, the instantaneous frequency (IF) trajectory of the rotating shaft is accurately represented by the better time-frequency resolution of IMSST, and the demodulation phase function for GD is constructed by combining the fault characteristic coefficient (FCC) of the rolling bearing. Then, the components in the signal that match the demodulation phase function are mapped into constant IF components. Through adaptive EFD and inverse demodulation, the time-varying IF components satisfying the conditions are separated. Finally, based on the Hilbert transform (HT) and adaptive time-frequency spectrum (ATFS), a high-quality time-frequency representation (TFR) is reconstructed to extract the time-varying IF fault features and identify the defect type of rolling bearing. The simulation and measured signal analysis results show that the proposed method can accurately extract fault features of rolling bearings with different defects under variable speed and identify fault types, thus maximally avoiding the influence of noise and interference components.

Optimal symbolic entropy: An adaptive feature extraction algorithm for condition monitoring of bearings

Due to their effectiveness in vibration-based fault feature extraction from bearings, entropy-based methods have become a hot research topic. Symbolic dynamic filtering reduces background noise in bearing signals, making it ideal for entropy analysis. However, the partitioning approach selection of symbolic dynamic filtering mainly depends on experience, which may bring over-track and under-track phenomena. The optimal symbolic entropy (OSE) method proposes a solution by using mean spectral kurtosis to evaluate the symbolization performance of partitioning approaches. This method improves the identification of bearing faults through steps such as evaluating frequency and amplitude preservation, selecting the optimal symbolization approach, and using the OSE method with multiscale analysis. Simulative and experimental data analysis demonstrates its superior ability to extract bearing fault characteristics, with better performance and robustness than existing methods.

A hybrid method of frequency-weighted energy operator and power spectrum fusion to detect bearing faults

The difficulties in early fault diagnosis of bearings mainly include two aspects: first, the initial damage size of the bearing is small, and the abnormal vibration caused by slight damage to the bearing is very weak. Second, vibration signals collected in actual industrial environments always contain strong noise interference. Therefore, traditional diagnostic procedures are not satisfactory. To address these challenges, this work provides a hybrid model combining frequency-weighted energy operator (FWEO) with power spectrum fusion (PSF) to identify weak fault features of bearings and detect different fault types. Different from traditional time-domain signal filtering, PSF is first used to reduce the interference of noise components in the power spectrum, which will not weaken the fault signal components during denoising. Second, the filtered signal is transformed into the time domain and FWEO is employed to further enhance the cyclic fault signal caused by the weak defect of the bearing. Finally, the existence of a fault is identified by observing the squared envelope spectrum of the signal. The effectiveness of the proposed hybrid model is demonstrated through two simulated fault signals and three different experimental fault signals. The results show that the proposed model has high anti-noise performance and robustness and can extract the fault frequency well.

An improved decomposition method for extracting unknown rolling bearing fault features from strong noise

An improved decomposition method for extracting unknown rolling bearing fault features from strong noise

Multi-task neural network blind deconvolution and its application to bearing fault feature extraction

Blind deconvolution (BD) is an effective method to extract fault-related characteristics from vibration signals. Previous researches focused on two primary approaches to improve the robustness and effectiveness of BD methods: developing new optimization functions or devising new methods for estimating filter coefficients. However, these methods often suffer from the difficulty of finding the global optimum due to the complex non-convex functions. To address this issue, we propose a novel multi-objective criterion, combining two well-established sparsity criteria: kurtosis and norm, that evaluates signal characteristics in both the time and frequency domains. We observe that this criterion, consisting of two sparsity criteria with opposite monotonicity, can mutually constrain and avoid overfitting that occurs with single-domain optimization. Inspired by multi-task convolutional neural networks, we introduce a multi-task 1DCNN with two branches to optimize the criterion in both domains simultaneously. To our best knowledge, it is the first time a multi-task convolutional neural network is used for BD problems. Experiments show that our method outperforms other state-of-the-art BD methods. We have share our code in https://github.com/asdvfghg/MNNBD

Study on fault diagnosis of ultra-low-speed rolling bearings based on full vector sound spectrogram

By exploring the mapping relationship between the multi-directional data and fault characteristics of bearings, a time-frequency analysis method for considering the multi-directional acoustic emission (AE) data of bearings is proposed. Firstly, using the full vector spectrum (FVS) theory, the full vector sound spectrogram of the dual-channel AE signal of a bearing is extracted to enhance the representation of the fault state using time-frequency characteristics. Then, the obtained full vector sound spectrogram is transformed into a specific size as the input feature map and a convolutional neural network (CNN) classifier model is established. Next, the Softmax classifier is used to classify the bearing faults in order to realise the intelligent fault diagnosis of an ultra-low-speed rolling bearing. The comparison of the different models shows that the average recognition accuracy using the full vector sound spectrogram CNN model can reach 95.61%, which is better than the other three methods. The feature extraction using the full vector sound spectrogram feature analysis method has a high degree of recognition for bearing faults in an ultra-low-speed state and can provide high accuracy and stability under noisy conditions.

Rolling bearing fault feature selection based on standard deviation and random forest classifier using vibration signals

The precise identification of faults is vital for ensuring the reliability of the bearing’s performance, and thus, the functionality of rotary machinery. The focus of our study is on the role that feature selection plays in improving the accuracy of predictive models used for diagnosis. The study combined the Standard Deviation (STD) parameter with the Random Forest (RF) classifier to select relevant features from vibration signals obtained from bearings operating under various conditions. We utilized three databases with different bearings’ health states operating under distinct conditions. The results of the study were promising, indicating that the proposed method was not only effective but also consistent, even under time-varying conditions.

Incipient detection of bearing fault using impulse feature enhanced weighted sparse representation

The bearing fault impact impulses induced by the contact between components with drawback, is difficult to be detected at sprouting stage due to the interference of background noise, harmonics, random shocks, etc. In this paper, an impulse feature enhanced weighted sparse representation (IFEWSR) algorithm is proposed to accurately detect the weak bearing fault impact feature from incipient stage condition monitoring (CM) signal. Firstly, a modified fault period estimation method is presented to improve the robustness and reduce the computational complexity of recently proposed algorithms. Secondly, a novel weighting strategy on wavelet coefficients, indicated by the period-assisted corelated kurtosis of envelope spectrum (CKSES), is presented to denote the contribution of subband signals for sparse representation calculation framework. In addition, the mean normalized energy-weight deviation (MNEWD) rule is proposed to evaluate the performance of the weighting algorithm on subband signals which is blank at present. Thirdly, a novel fault feature enhancement technique is developed to better capture the bearing fault feature information. The effectiveness and superiority of the proposed method are proved by simulation and experiments. Results show that the proposed IFEWSR method provides higher accuracy for incipient fault feature extraction and outperforms other state-of-the-art methods.

Blind Deconvolution Based on Correlation Spectral Negentropy for Bearing Fault.

Blind deconvolution is a method that can effectively improve the fault characteristics of rolling bearings. However, the existing blind deconvolution methods have shortcomings in practical applications. The minimum entropy deconvolution (MED) and the optimal minimum entropy deconvolution adjusted (OMEDA) are susceptible to extreme values. Furthermore, maximum correlated kurtosis deconvolution (MCKD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) are required prior knowledge of faults. On the basis of the periodicity and impact of bearing fault signals, a new deconvolution algorithm, namely one based on maximum correlation spectral negentropy (CSNE), which adopts the particle swarm optimization (PSO) algorithm to solve the filter coefficients, is proposed in this paper. Verified by the simulated vibration model signal and the experimental simulation signal, the PSO-CSNE algorithm proposed in this paper overcomes the influence of harmonic signals and random pulse signals more effectively than other blind deconvolution algorithms when prior knowledge of the fault is unknown.

Early fault feature extraction for rolling bearings using adaptive variational mode decomposition with noise suppression and fast spectral correlation

To accurately extract fault information from rolling bearing (RB) vibration signals with strong nonlinear and non-stationary characteristics, a novel method using adaptive variational mode decomposition with noise suppression and fast spectral correlation (AVMDNS-FSC) is proposed. The AVMDNS algorithm can adaptively select VMD parameters K and α, which reduces the error caused by the improper selection of VMD parameters based on experience or prior knowledge of the signal. Meanwhile, the AVMDNS also effectively suppresses noise in intrinsic mode function (IMFs) and avoids unexpected removal of the IMFs containing important fault information. In addition, the FSC can further suppress residual noise and interference harmonics to enhance the periodic fault pulses and hence accurately extract bearing fault features. Simulation analysis and experimental studies are carried out through comparison with other methods. Results show that the AVMDNS-FSC method has higher sensitivity and effectiveness in extracting early periodic fault pulses of RB vibration signals.

Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

As parameter independent yet simple techniques, the energy operator (EO) and its variants have received considerable attention in the field of bearing fault feature detection. However, the performances of these improved EO techniques are subjected to the limited number of EOs, and they cannot reflect the non-linearity of the machinery dynamic systems and affect the noise reduction. As a result, the fault-related transients strengthened by these improved EO techniques are still subject to contamination of strong noises. To address these issues, this paper presents a novel EO fusion strategy for enhancing the bearing fault feature nonlinearly and effectively. Specifically, the proposed strategy is conducted through the following three steps. First, a multi-dimensional information matrix (MDIM) is constructed by performing the higher order energy operator (HOEO) on the analysis signal iteratively. MDIM is regarded as the fusion source of the proposed strategy with the properties of improving the signal-to-interference ratio and suppressing the noise in the low-frequency region. Second, an enhanced manifold learning algorithm is performed on the normalized MDIM to extract the intrinsic manifolds correlated with the fault-related impulses. Third, the intrinsic manifolds are weighted to recover the fault-related transients. Simulation studies and experimental verifications confirm that the proposed strategy is more effective for enhancing the bearing fault feature than the existing methods, including HOEOs, the weighting HOEO fusion, the fast Kurtogram, and the empirical mode decomposition.

AFARN: Domain Adaptation for Intelligent Cross-Domain Bearing Fault Diagnosis in Nuclear Circulating Water Pump

Domain adaptation can transfer cross-domain diagnosis knowledge by minimizing the divergence of labeled source and unlabeled target data. However, the model neglects to maximize physics prior knowledge during feature extraction and distribution alignment, resulting in a noninterpretable model even negative transfer. Hence, a physics-informed domain adaptation network, termed adaptive fault attention residual network (AFARN), is proposed. First, an adaptive fault attention mechanism is designed to refine features guided by bearing fault characteristics, suited to generating diagnosis-relevant features. Then, several metrics are applied to minimize the marginal and conditional distribution discrepancy of features, thus, generalizing the model from source to target domain. The AFARN utilizes the fault characteristics and label information simultaneously to train the model, which can enhance the distribution alignment of diagnosis-relevant features, thus, providing an interpretable knowledge transfer. Finally, experiments on public and circulating water pump datasets show that AFARN can enhance fault feature learning and diagnosis accuracy.

Squared envelope sparsification via blind deconvolution and its application to railway axle bearing diagnostics

A sparse squared envelope is crucial for efficient and accurate diagnosis of bearing faults. Blind deconvolution (BD) is a well-established sparse feature enhancement method for the diagnostics of rolling bearings. Traditional BD methods, such as minimum entropy deconvolution (MED), are susceptible to random transients, making it difficult to enhance fault features of rolling bearings subject to strong random shocks. Deconvolution methods that take the fault characteristic frequency (or fault impulse period) of interest as an algorithm input parameter, such as maximum second-order cyclostationarity blind deconvolution (CYCBD), can alleviate this deficiency. However, bearing fault features are difficult to be enhanced by these methods when the specified characteristic frequency deviates from the actual value greatly. To overcome these problems, the modified smoothness index (MSI) of the squared envelope is proposed as the objective function of the deconvolution method, and a new BD method is developed to achieve a sparse squared envelope for fault diagnosis of rolling bearings. Furthermore, the methodology is extended to the frequency domain, and another new BD method that utilizes the MSI of the squared envelope spectrum as the objective function is established to achieve a sparse squared envelope spectrum for bearing diagnostics. These two proposed BD methods are robust to random transients and do not require characteristic frequency or impulse period as an input parameter for feature enhancement. The performance of the two proposed BD methods is verified on experimental datasets from two different railway axle bearing test rigs and compared with the state-of-the-art deconvolution methods. The results show that the two proposed methods can effectively enhance repetitive transient features in noisy vibration signals and accurately diagnose different faults of railway axle bearings.

An adaptive group sparse feature decomposition method in frequency domain for rolling bearing fault diagnosis

Group-sparse mode decomposition (GSMD) is a decomposition method designed based on the group sparse property of signals in frequency domain. It is proved to be highly efficient and robust against noise, showing bright prospects for bearing fault diagnosis. However, the following adverse factors may impede its application for incipient bearing fault feature extraction: Initially, the GSMD method did not consider the impulsiveness and periodicity of the bearing fault feature. As a result, the ideal filter bank generated by GSMD may not accurately cover the fault frequency band because it may produce over-coarse or over-narrow filter bank under the condition of strong interference harmonics, large random shocks and heavy noise. Moreover, the location of informative frequency band was obstructed since the bearing fault signal shows complicated distribution in frequency domain. To overcome the abovementioned limitations, an adaptive group sparse feature decomposition (AGSFD) method is proposed. Firstly, the harmonics, large-amplitude random shocks and periodic transient feature are modeled as limited bandwidth signals in the frequency domain. On this basis, an autocorrection of envelope derivation operator harmonic to noise ratio (AEDOHNR) indicator is proposed to guild the construction and optimization of the filter bank of AGSFD. Also, the regularization parameters of AGSFD are adaptively determined. With the optimized filter bank, the original bearing fault is decomposed into a serial of components with AGSFD method, where the sensitive fault-induced periodic transient component is maintained using the AEDOHNR indicator. Finally, the studies of the simulation and two experimental items are carried out to evaluate the feasibility and the superiority of AGSFD method. The results indicate the AGSFD method can identify the early failure in the presence of heavy noise, strong harmonics or random shocks and has better decomposition efficiency.

Enhanced transfer learning method for rolling bearing fault diagnosis based on linear superposition network

Deep transfer learning is used to solve the problem of unsupervised intelligent fault diagnosis of rolling bearings. However, when the data distribution between two domains is different, the existing deep transfer learning models which only rely on the domain-invariant features are not enough to complete the target domain data learning. To solve this problem, an enhanced transfer learning method based on the linear superposition network is proposed for rolling bearing fault diagnosis. This method improves the structure of the one-dimensional convolutional neural network (1D-CNN) by constructing linear superposition convolution blocks. At the same time, the loss function of transfer learning is constructed by using the pseudo-label of the target domain from the network, which enhances the ability of rolling bearing fault feature extraction. Compared with the traditional feature-based transfer learning methods, the proposed enhanced transfer learning method based on the linear superposition network can make the network place more stress on the feature learning of the target domain. Experimental results on the Paderborn University (PU) dataset show that, compared with the improved deep adaptation network (DAN) model, the proposed method improves the average diagnosis accuracy by 21% on six transfer tasks, showing improved bearing fault diagnostic precision.

Fault diagnosis of sensor pulse signals based on improved energy fluctuation index and VMD

Variational mode decomposition (VMD) has been widely applied in sensors. However, the mode number and balance parameter seriously limit VMD application. To solve this problem, this study proposes a novel method, which combines an improved energy fluctuation index (IEFI) and modified VMD (MVMD). In the proposed method, IEFI provided better performance to resist interference from random impulses by considering the periodicity of fault feature components. Consequently, it is applied to determine the initial center frequency for MVMD, which fixed the problem of the mode number. Moreover, a novel balance parameter search strategy, which can adaptively determine the optimal balance parameter, is combined with MVMD whose stop condition is replaced by kurtosis to extract the fault feature. Simulation results indicated that the proposed method does well in detecting the feature of a periodic impulse signal from the signal polluted by some interference impulses. Moreover, the bearing fault diagnosis results demonstrate that the proposed method can accurately detect bearing fault features. Furthermore, the method was validated with bearing fault data. The results showed that the method can accurately extract the fault characteristics of the impulse signal and achieve fault diagnosis.

Extraction of incipient fault features of rolling bearings based on CWSSMD and 1.5D-EDEO demodulation

The fault feature of a rolling bearing is weak in the incipient fault stage, with severe environmental noise interference, which makes it difficult to extract the fault feature information from the vibration signal. In this paper, an adaptive method based on component-weighted symplectic singular mode decomposition and 1.5-dimensional envelope derivative energy operator (1.5D-EDEO) demodulation is proposed to extract the incipient fault features of a bearing and it does not require manual parameter setting. The method begins with the original vibration signal decomposed by symplectic singular mode decomposition to obtain multiple initial symplectic singular components (ISSCs). Then, the fault information amount of the ISSCs is measured by fault impulse sparsity (FIS) constructed by the Gini index of the square envelope which has a powerful sparsity measurement capability. After this, the ISSCs are reconstructed based on the weights obtained from the FIS to obtain the final denoised symplectic singular component (DSSC). Finally, the DSSC is demodulated by 1.5D-EDEO to further highlight the fault features of the bearing and reduce noise interference. The effectiveness of the proposed method is verified through simulation and experimental analysis. The experimental results show that the proposed method is more effective in enhancing incipient bearing fault features compared to other bearing fault diagnosis methods.

Rolling Bearing Fault Feature Selection Method Based on a Clustering Hybrid Binary Cuckoo Search

In order to solve the low accuracy in rolling bearing fault diagnosis caused by irrelevant and redundant features, a feature selection method based on a clustering hybrid binary cuckoo search is proposed. First, the measured motor signal is processed by Hilbert–Huang transform technology to extract fault features. Second, a clustering hybrid initialization technique is given for feature selection, combining the Louvain algorithm and the feature number. Third, a mutation strategy based on Levy flight is proposed, which effectively utilizes high-quality information to guide subsequent searches. In addition, a dynamic abandonment probability is proposed based on population sorting, which can effectively retain high-quality solutions and accelerate the convergence of the algorithm. Experimental results from nine UCI datasets show the effectiveness of the proposed improvement strategy. The open-source bearing dataset is used to compare the fault diagnosis accuracy of different algorithms. The experimental results show that the diagnostic error rate of this method is only 1.13%, which significantly improves classification accuracy and effectively realizes feature dimension reduction in fault datasets. Compared to similar methods, the proposed method has better comprehensive performance.

Feature Mining and Sensitivity Analysis with Adaptive Sparse Attention for Bearing Fault Diagnosis

Bearing fault diagnosis for equipment-safe operation has a crucial role. In recent years, more achievements have been made in bearing fault diagnosis. However, for the fault diagnosis model, the representation and sensitivity of bearing fault features have a great influence on the diagnosis output results; thus, the attention mechanism is particularly important for the selection of features. However, global attention focuses on all sequences, which is computationally expensive and not ideal for fault diagnosis tasks. The local attention mechanism ignores the relationship between non-adjacent sequences. To address the respective shortcomings of global attention and local attention, an adaptive sparse attention network is proposed in this paper to filter fault-sensitive information by soft threshold filtering. In addition, the effects of different signal representation domains on fault diagnosis results are investigated to filter out signal representation forms with better performance. Finally, the proposed adaptive sparse attention network is applied to cross-working conditions diagnosis of bearings. The adaptive sparse attention mechanism focuses on the signal characteristics of different frequency bands for different fault types. The proposed network model achieves better overall performance when comparing the cross-conditions diagnosis accuracy and model convergence speed.

Fault diagnosis of printing press bearing based on deformable convolution residual neural network

<abstract> <p>Rolling bear is a major critical component of rotating machinery, as its working condition affects the performance of the equipment. As a result, the condition monitoring and fault diagnosis of bearings get more and more attention. However, the strong background noise makes it difficult to extract the bearing fault features exactly. Furthermore, regular gradient disappearance and overfit appear in traditional network model training. Therefore, taking the printing press bearings as the research object, an intelligent fault diagnosis method based on strong background noise is proposed. This method integrates frequency slice wavelet transform (FSWT), deformable convolution and residual neural network together, and realizes the high-precision fault diagnosis of the printing press bearings. First, FSWT is used to preprocess the original vibration signal to obtain bearing fault features in the time and frequency domain, reconstruct the signal in any frequency band and describe local features accurately. Second, the ResNet is selected as the base network, and the two-dimensional time-frequency diagrams (TFD) obtained by preprocessing are used as input. For the model that has a poor ability to extract subtle features under strong background noise, the deformable convolution layer is introduced to reconstruct the convolution layer of ResNet, called deformable convolution residual neural network (DC-ResNet). Finally, the effectiveness of this method is verified by using the data sets collected under experimental conditions and actual working conditions for fault diagnosis of the printing press. The results show that the DC-ResNet can classify different bearing faults under strong background noise, and the accuracy and stability are greatly improved, which the accuracy meets 93.90%. The intelligent fault diagnosis with high-precision of printing press bearings under complex working conditions is realized by the proposed method.</p> </abstract>