Rolling Bearing Vibration Signal Research Articles

Life prediction method of rolling bearing based on CNN-LSTM-AM

Bearing is the key component to determine the health of machinery, and it is of great significance to monitor its working status in real time and predict its remaining useful life. In recent years, the RUL prediction method based on deep learning has been widely used and achieved good prediction results. Here, a bearing life prediction method based on convolution neural network (CNN), long short term memory (LSTM) and attention mechanism (AM) is proposed. First of all, the time domain and frequency domain features of the original vibration signals of rolling bearings are extracted, and the extracted feature set is normalized as the input of CNN. The main function of CNN is to extract spatial features and reduce the dimension of the data. Then, using LSTM to extract the information that may be ignored by CNN, the feature information extracted by CNN-LSTM is input to the attention mechanism for weighting, and the key information is screened. And then more accurately represent the degradation characteristics of the equipment, and finally get the bearing remaining life. The performance of the model is verified by two sets of public data sets, and the experimental results show that it is compared with the CNN-LSTM method. The root mean square error (RMSE) index based on CNN-LSTM-AM method is reduced by 14.6 % and 13.8 % respectively, and the score index is increased by 2.0 % and 2.4 % respectively. The results show that the proposed method has higher accuracy in bearing RUL prediction.

PSO-optimized SSLMS adaptive filter for signal denoising of rolling bearings under small sample condition

Abstract To address the issue that the deep learning-based denoising algorithms can hardly effectively eliminate the background noise under small sample data condition, this paper proposes a new denoising method based on spectral subtraction (SS) and least mean square (LMS) adaptive filtering algorithms. To achieve the adaptive selection for the parameters of SS and LMS algorithms, particle swarm optimization (PSO) approach is employed to search and optimize the parameters in the two algorithms, which is helpful for the two algorithms to play an important role in eliminating the noise components with the different properties. Subsequently, the SS algorithm and the LMS algorithm are appropriately combined, and the SS-processed signal is input into the LMS algorithm as a desired signal to actualize the LMS adaptive filtering function. In this way, the denoising performance of both algorithms can be maximally utilized, which achieves effective noise reduction in vibration signal. The effectiveness and superiority of the proposed method are validated through simulation data and rolling bearing experiment data, respectively. The results demonstrate that the proposed method significantly diminishes noise components and retains precise and reliable fault features under small sample data condition, which provides an effective denoising method for rolling bearing vibration signals under small sample data condition in practical engineering scenarios.

Research on Feature Extraction and Fault Diagnosis Method for Rolling Bearing Vibration Signals Based on Improved FDM-SVD and CYCBD

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.

Research on the Application of Variational Mode Decomposition Optimized by Snake Optimization Algorithm in Rolling Bearing Fault Diagnosis

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.

Multiscale permutation entropy based on natural visibility graph and its application to rolling bearing fault diagnosis

Rolling bearings being important components of mechanical equipment, the accurate fault diagnosis method of rolling bearings is of great importance to ensure production safety. Permutation entropy is a nonlinear measure of the irregularity of time series, which involves calculating permutation patterns, that is, defining permutations by comparing adjacent values of the time series. When using graph signal processing technology to analyze the vibration signal of rolling bearing, the natural visibility graph (NVG) can better reflect the dynamic characteristics of the vibration signal than path graph (PG). In this paper, the multiscale permutation entropy (MPE) is defined on NVG, and it is used to characterize the different fault characteristics of rolling bearings. The sand cat swarm optimization (SCSO) algorithm is employed to optimize the parameters of support vector machine (SVM); The MPEs of different faults of rolling bearing which defined on NVG are regarded as the fault feature set input into optimized SVM, and it is applied to characterize the different fault characteristics of rolling bearings, realizing fault diagnosis of rolling bearing. The proposed method is used to analyze the experimental data which contain both normal and faulty rolling bearings. The experiment results show that the proposed method can diagnose the bearing faults effectively. The MPE based on NVG is superior to MPE based on PG and MPE based on the vibration signal in distinguishing the different damage states of rolling bearings. The classification accuracy of optimized SVM based on SCSO algorithm is higher than other classical models. The effectiveness and feasibility of defining entropy on the graph signal and as the fault feature vectors for rolling bearing to realize fault diagnosis is validated. The results indicate that the proposed method can effectively detect bearing faults, and demonstrate its effectiveness and robustness for rolling bearing fault diagnosis.

Rolling bearing degradation trend prediction based on composite multiscale grey entropy and dynamic particle filter

Abstract High accuracy prediction of degradation trend provides valuable information in establishing reasonable maintenance decision-making with the goal of improving the maintenance efficiency and avoiding sudden downtime. The extraction of degradation features and the prediction algorithm are the key factors in degradation trend prediction. In this work, based on composite multiscale grey entropy (CMGE) and dynamic particle filter (PF), a novel prediction architecture is proposed to improve accuracy under different working conditions. The CMGE is proposed as the degradation feature indicator extracted from rolling bearing vibration signal. The dynamic PF is proposed to predict the degradation trend of rolling bearing. Three rolling bearing accelerated life tests were conducted to evaluate the performance of the proposed method for rolling bearing degradation trend prediction. Experimental results demonstrate CMGE has good monotonicity and weak data length dependence, which can effectively describe the degradation trend of rolling bearing, and the proposed dynamic PF achieves higher prediction accuracy than the traditional PF and GM model, respectively.

SEACKgram: a targeted method of optimal demodulation-band selection for compound faults diagnosis of rolling bearing

Rolling bearing plays an important role in carrying and transmitting power in rotating machinery, and the bearing fault is easy to lead to mechanical accidents, resulting in huge losses and casualties. Therefore, the condition monitoring and diagnosis of rolling bearings are very important to improve the safety of equipment. Compound fault is a common fault evolved from the initial defect, which is characterized by randomness, coupling, concealment, and secondary. The existence of these characteristics brings great challenges to the accurate diagnosis of compound faults. In the diagnosis of compound faults, the traditional methods that select the single optimal demodulation frequency band for analysis and identification sometimes cannot completely extract multiple fault components, which are prone to miss diagnosis and misdiagnosis. In order to solve this problem, the SEACKgram method is proposed by constructing a Square Envelope Unbiased Autocorrelation Correlation Kurtosis (SEACK) index. The frequency band of the original signal is divided by the Maximal Overlap Discrete Wavelet Packet Transform, and the SEACK index is used to quantitatively describe the fault signals of different frequency bands. According to the different fault periods, the resonant frequency bands of the maximum SEACK value are selected, then the resonance band signal is analyzed by square envelope spectrum, and the fault type is identified according to the fault characteristic frequency. The simulated and experimental vibration signals of rolling bearings with compound faults are used to verify the feasibility of the proposed method. The results show that the proposed SEACKgram can improve the accuracy of compound faults identification and would be applied in engineering practice to a certain extent.

Improved SE-ResNet Acoustic–Vibration Fusion for Rolling Bearing Composite Fault Diagnosis

An enhanced fault diagnosis approach for rolling bearings with composite faults using an optimized Squeeze and Excitation ResNet (SE-ResNet) model is proposed. This method integrates grid search (GS), support vector regression (SVR), ensemble empirical mode decomposition (EEMD), and low-rank multimodal fusion (LMF) to effectively handle the signals of acoustic–vibration fusion. By combining these techniques, the aim is to improve the accuracy and reliability of rolling bearing fault diagnosis. Firstly, improved EEMD combined with GS-SVR and a window function is used for rolling bearing vibration signal decomposition. Singular value methods are used to filter and reconstruct the results. Secondly, Markov transition fields (MTFs) are used to encode vibration signals into 2D images. LMF is used for the fusion of vibration and sound signals. An improved Squeeze and Excitation ResNet50 network is proposed for feature identification and classification of rolling bearing composite fault data. Finally, the method undergoes rigorous testing and evaluation using rolling bearing data. The experimental outcomes demonstrate that, in comparison to traditional neural networks, the enhanced SE-ResNet, integrated with GS-SVR-EEMD and LMF, attains superior diagnostic accuracy. Additionally, the proposed approach can be effectively utilized for diagnosing rolling bearing composite faults.

Fault diagnosis of rolling bearing based on SEMD and ISSA-KELMC

Enhancing the operational reliability of rotary machinery relies significantly on the effective diagnosis of faults in rolling bearings. This study introduces an innovative method to improve the accuracy of fault diagnosis of rolling bearings during operation. First, we propose a sine empirical mode decomposition (SEMD) designed to effectively mitigate mode mixing and decompose the vibration signals of rolling bearings into a series of intrinsic mode functions. Subsequently, we constructed and optimized a kernel extreme learning machine classifier (KELMC) using the improved sparrow search algorithm (ISSA). Within ISSA, the opposition-based Learning method is refined and applied to enhance the optimization performance of the sparrow search algorithm. Finally, the paper presents a novel method for the fault diagnosis of rolling bearings based on SEMD and ISSA-KELMC, which can effectively extract the fault features and accurately recognize the fault types of rolling bearings by taking advantage of the SEMD and ISSA-KELMC. The effectiveness of the proposed method was verified through two simulation and fault diagnosis experiments. The results demonstrated the efficiency of the method in diagnosing faults in rolling bearings under both consistent and variable working conditions. This approach is valuable for fault diagnosis and condition monitoring of rotating machinery.

Fault diagnosis method using MVMD signal reconstruction and MMDE-GNDO feature extraction and MPA-SVM

To achieve a comprehensive and accurate diagnosis of faults in rolling bearings, a method for diagnosing rolling bearing faults has been proposed. This method is based on Multivariate Variational Mode Decomposition (MVMD) signal reconstruction, Multivariate Multiscale Dispersion Entropy (MMDE)-Generalized Normal Distribution Optimization (GNDO), and Marine predators’ algorithm-based optimization support vector machine (MPA-SVM). Firstly, by using a joint evaluation function (energy*|correlation coefficient|), the multi-channel vibration signals of rolling bearings after MVMD decomposition are denoised and reconstructed. Afterward, MMDE is applied to fuse the information from the reconstructed signal and construct a high-dimensional fault feature set. Following that, GNDO is used to select features and extract a subset of low-dimensional features that are sensitive and easy to classify. Finally, MPA is used to realize the adaptive selection of important parameters in the SVM classifier. Fault diagnosis experiments are carried out using datasets provided by the Case Western Reserve University (CWRU) and Paderborn University (PU). The MVMD signal reconstruction method can effectively filter out the noise components of each channel. MMDE-GNDO can availably mine multi-channel fault features and eliminate redundant (or interference) items. The MPA-SVM classifier can identify faults in different working conditions with an average accuracy of 99.72% and 100%, respectively. The results demonstrate the accuracy, efficiency, and stability of the proposed method.

Multi-scale residual neural network with enhanced gated recurrent unit for fault diagnosis of rolling bearing

As the key component of rotating machinery, effective and reliable fault diagnosis of rolling bearing is particularly critical for promoting production safety and economic benefits. The powerful representation learning ability of convolutional neural network (CNN) enables it to effectively extract fault information from vibration signals of rolling bearing. Nevertheless, challenges are faced by CNN in extracting features at multi-scale and capturing temporal features. With regard to this issue, a hybrid deep learning model that incorporates the multi-scale residual neural network (MSRN) with the enhanced gated recurrent unit (EGRU), namely MSRN-EGRU, is proposed in this paper. To begin with, MSRN is designed by introducing multi-scale structure and residual connections into CNN for extracting local features effectively and improving the feature representation of the model. Then, the extracted local features are input into EGRU to further extract temporal features, where EGRU is proposed by improving GRU structure and embedding scaled exponential liner unit (SELU), which enhances the nonlinear modeling and memory ability. Eventually, the obtained features are processed by α-Dropout and global average pooling before being inputted into the softmax layer for fault diagnosis. To validate the effectiveness of the proposed model, three baseline models and two ablation models were employed for comparative experiments with two bearing datasets. The experimental results reveal that the proposed model achieves commendable performance in terms of accuracy, robustness, and convergence for fault diagnosis of rolling bearing.

An Automatic Parameter Setting Variational Mode Decomposition Method for Vibration Signals

Variational mode decomposition (VMD) provides a feasible approach to decompose vibration signals obtained from complex machinery for further applications. The mode frequency bandwidth control parameter and the total number of modes are critical parameters for VMD. Thus, optimally and automatically setting the two parameters is an essential issue for VMD for various practical vibration signal sources. To this end, this work proposes an automatic parameter setting VMD for vibration signals. First, we use the bandwidth evaluation criterion and the mean mode-location distance to evaluate the sparsity of modes; we use the energy loss to evaluate the reconstruction from modes. Then, we synthesize the three criteria into a novel optimization model using logarithmic transformation. Accordingly, a genetic algorithm-based solver is developed for the optimization model. Finally, the artificial multi-component signal and the practical rolling bearing vibration signal from CWRU Laboratory are used to verify the performance of the proposed optimization method.

Unsupervised rolling bearing fault diagnosis method across working conditions based on multiscale convolutional neural network

In practical engineering, the features of rolling bearing vibration signals often vary in distribution under different working conditions, and obtaining sample labels for target working conditions can be challenging. Therefore, a multi-scale depth subdomain adaptive convolutional neural network (MS-DSACNN) fault diagnosis method is proposed. The MS-DSACNN method is based on a multi-scale feature extraction method combined with an improved 1D-ConvNeXt architecture, which fuses low- and high-level features into multi-scale fault features. A channel attention mechanism is also introduced to adaptively assign weights to the fused multi-scale features. The local maximum mean discrepancy is constructed by extracting features and their corresponding source and pseudo-label inputs for the source and target working conditions, respectively, to construct the loss function for backpropagation. The MS-DSACNN model was evaluated in experiments with two different loads and one variable speed dataset. The experimental results indicate that MS-DSACNN has higher feature extraction capability and diagnostic accuracy than other unsupervised cross-condition fault diagnosis techniques. Additionally, the scenario where the signal captured by the sensor contains a considerable amount of noise in actual working conditions is considered.

Deep learning based on rolling bearing fault diagnosis method

With the continuous development of industrial automation, rolling bearings play a crucial role in many fields as key mechanical components, and the fault diagnosis of rolling bearings have great significance. This paper discusses a deep learning based rolling bearing fault diagnosis method, aiming to improve the accuracy and efficiency of fault detection. Firstly, the vibration signals of rolling bearings are pre-processed to extract the feature information that helps fault diagnosis. Then, the features were automatically learned and classified by using BP neural network. Finally, the effectiveness and robustness of the method were verified through experiments. Compared with the traditional fault diagnosis method, the deep learning-based rolling bearing fault diagnosis method has higher accuracy and practicality, which provides strong support for the fault detection and preventive maintenance of rolling bearings.

A life prediction method of rolling bearing based on signal reconstruction and fusion dual channel network

In addressing the problem of low prediction accuracy in the remaining useful life (RUL) prediction of rolling bearings, caused by noise interference and insufficient extraction of sensitive features by deep learning models, this paper presents a life prediction method based on signal reconstruction and dual-channel network fusion. First, addressing the issue of extracting weak features from rolling bearing vibration signals, an optimized combination of variational mode decomposition (VMD) and Teager–Kaiser energy operator (TKEO) for signal reconstruction is proposed. TKEO is used to track the energy of high-frequency impulses in the original signal. The sparrow search algorithm is employed for optimizing VMD to perform high-frequency filtering, resulting in an optimized impulse energy signal. This signal is then multiplied with the original signal to enhance weak impulse features under noisy background. Next, a dual-channel network model for RUL prediction is constructed based on temporal convolutional network (TCN) and convolutional neural networks (CNNs). The one-dimensional time-series signal obtained after reconstruction and denoising serves as the input for the TCN network channel. Simultaneously, the signal obtained after reconstruction and denoising undergoes synchrosqueezed wavelet transforms to generate a two-dimensional time–frequency representation as input for the CNN network channel. This setup allows TCN and CNN to respectively extract temporal features from the vibration signal and time–frequency features from the spectrogram. By incorporating self-attention mechanisms, internal correlations between different features are fully explored, thereby enhancing prediction accuracy.

Multiple faults separation and identification of rolling bearings based on time-frequency spectrogram

Rolling bearings play a crucial role as components in rotating machinery across various industrial fields. Bearing faults can potentially lead to severe accidents in operating machines. Therefore, condition monitoring and fault diagnosis of rolling bearings are essential for preventing equipment failures. Multiple faults are a common occurrence resulting from the prolonged operation of rolling bearings, and numerous research efforts have been made to study multiple faults in different components of the bearing. However, diagnosing multiple faults in a single component of the rolling bearing still remains a highly challenging task. In this paper, a multiple faults separation and identification method based on time-frequency (TF) spectrogram (TFS) is proposed for vibration signals of rolling bearings. Firstly, the fast path optimization method is improved to match the TFS of original vibration signals in bearing faults generated by short-time Fourier transform. Then multiple TF curves are extracted from the TFS by the proposed multiple transient component curves extraction method based on the improved fast path optimization method. With the fault characteristic period, a classification criterion is introduced to separate TF curves. Secondly, a TF masking method is constructed to retain the TF information closely related to fault components of vibration signals. Finally, the novel TF representation can be obtained to develop signal reconstruction, and multiple faults can be detected based on envelope analysis separately. The experiments from rolling bearings with multiple faults on raceways are used to verify the effectiveness of the proposed methods.

Research on fault feature extraction and early warning of rolling bearing vibration signal of generator set

Abstract The study of fault feature extraction and early warning of rolling bearing vibration signal of generator sets is beneficial for the timely diagnosis of bearing faults, thus improving the service life of generators. In this paper, a combined EEMD-GRU-MC prediction method is adopted to predict the model based on GRU through the data decomposition of EEMD, and the predicted model residuals are corrected using MC. The analysis and diagnosis of the algorithmic model are used to determine the fault characteristics of the generator’s vibration signals for diagnosis, and the analysis and diagnosis of the characteristics are verified using experiments with publicly available data sets from the Bearing Data Center at the Paderborn University School of Mechanical Engineering in Paderborn, Germany. Diagnosis can be performed with an accuracy of 99.6% under condition load K 0 and 99.9% close to 100% under condition load K 3 . The accuracy is the highest at this point, while the diagnostic accuracy at conditional load K 1 is only 99.8%, the lowest at this point. However, the accuracy under condition load K 2 is also 99.5%. Therefore, the diagnostic accuracy with EEMD-GRU-MC is around 99%. In this study, the EEMD-GRU-MC model can be used to extract the fault characteristics and early warning of the rolling bearing vibration signal of generator sets.

A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy

The vibration signals of rolling bearings are complex and changeable, and extracting meaningful features is difficult. Currently, the commonly used empirical mode decomposition (EMD) algorithms have the problem of mode aliasing. In this paper, a new feature extraction method based on the improved complete ensemble empirical mode decomposition with adapted noise (ICEEMDAN) and permutation entropy is proposed. In this method, the ICEEMDAN algorithm is first improved and optimized to enable a self-selection function The vibration signal is then decomposed into several intrinsic modal functions using this algorithm, and the permutation entropy is extracted as the fault feature of rolling bearings, which improves the accuracy of fault classification and realizes the intelligent feature extraction of different fault states. Then, the Case Western Reserve University dataset is used for verification, and the results show that this scheme can effectively separate the vibration signal characteristics of bearings in different states, and can be used to characterize the characteristics of different bearing signals. Finally, based on the mechanical transmission system bearing experimental platform independently developed by our school, the experimental results show that compared with the unimproved ICEEMDAN algorithm, the diagnostic accuracy rate of the proposed method is 99.5%, which is increased by 6.4%, and it can be effectively used for feature extraction of rolling bearings.

Incipient Fault Feature Extraction of Rolling Bearing Based on Signal Reconstruction

In the incipient fault vibration signals of rolling bearings, weak fault features are easily submerged in strong background noise and difficult to be extracted. The sparse decomposition method can perform well in the extraction of weak fault features, but the low signal-to-noise ratio (SNR) would cause excessive decomposition. To enhance the fault features and maintain the time–frequency structure of fault impulses, a novel incipient fault feature extraction of rolling bearing based on signal reconstruction is proposed. Firstly, the Teager energy operator (TEO) is used to obtain the envelope of the impulse components in the vibration signal, which is also sensitive to noise and would be seriously affected by strong noise. Secondly, a Savitzky–Golay (S–Golay) filter based on the particle swarm optimization (PSO) algorithm is adopted to suppress the noise in the TEO envelope and generate a smooth envelope signal. Finally, the fault signal is reconstructed by the multiplication of the filtered TEO envelope signal and the original signal. The reconstructed signal can maintain the structural characteristics of the original fault impact signal and can provide reliable feature enhancement signals for further sparse decomposition, multi-source vibration separation, and other operations. Simulation signals and experiments verify the effectiveness of this method in extracting early fault features under low SNRs.

Intelligent fault diagnosis of rolling bearings with low-quality data: A feature significance and diversity learning method

In real engineering applications, low-quality and insufficient vibration signals of rolling bearings, which are usually multiple faults with low signal-to-noise ratios (SNR), restricts the effective application of intelligent diagnosis methods based on deep learning. In this study, a new rolling bearing fault diagnostic method is proposed based on an improved deep fused convolutional neural network (DFCNN) combined with complementary ensemble empirical mode decomposition (CEEMD) and a short-time Fourier transform (STFT). First, the DFCNN is developed by introducing batch normalisation (BN) to overcome the gradient disappearance in convolutional neural networks (CNNs) and fuse multi-scale CNNs to improve feature diversity. Meanwhile, to enhance the quality of information, the original vibration signals are processed by CEEMD and STFT to obtain reconstructed denoising signals and time-frequency spectrograms, respectively. Then, these two types of data are input into a concatenation layer of DFCNN to extract the deep fault features, which are transformed by SoftMax to achieve bearing fault recognition. Finally, two bearing cases with data collected from the accelerated life degradation and different distributions are studied. The results reveal that the proposed method can fully mine fault features with low-quality data and realise efficient fault diagnosis.