Feature Extraction Of Vibration Signals Research Articles

Fault feature extraction and recognition of vibration signal based on CWD-SK and HMM

Aiming at the problem of rolling bearing fault diagnosis caused by interference information in the process of vibration transmission, this paper puts forward the theory and method of vibration signal fault diagnosis based on Choi Williams distribution spectral kurtosis (SK) combined with hidden Markov model (HMM). The fault location method of local mean decomposition (LMD) is used to screen the PF component with obvious fault characteristic information, CWD-SK is calculated and set as the characteristic parameter to realize the feature extraction of the initial fault of the vibration signal. CWD-SK is input into HMM as the feature vector to classify and identify the fault features of the vibration signal. Finally, the effectiveness of the algorithm is verified by the simulation of the actual data. It provides a robust theoretical and methodological basis for the establishment of fault diagnosis and classification recognition technology suitable for initial rolling mechanical vibration signals.

Intelligent Identification of Cavitation State of Centrifugal Pump Based on Support Vector Machine

In order to perform intelligent identification of the various stages of cavitation development, a micro high-speed centrifugal pump was used as a research object for vibration signal analysis and feature extraction for normal, incipient cavitation, cavitation and severely cavitated operating states of the pump at different temperatures (25 °C, 50 °C and 70 °C), based on support vector machines to classify and identify the eigenvalues in different cavitation states. The results of the study showed that the highest recognition rate of the individual eigenvalues of the time domain signals, followed by time frequency domain signals and finally frequency domain signals, was achieved in the binary classification of whether cavitation occurred or not. In the multi-classification recognition of the cavitation state, the eigenvalues of the time domain signals of the four monitoring points, the time frequency domain signals of the monitoring points in the X-direction of the inlet pipe and the Y-direction of the inlet pipe are combined, and the combined eigenvalues can achieve a multi-classification recognition rate of more than 94% for the cavitation state at different temperatures, which is highly accurate for the recognition of the cavitation state of centrifugal pumps.

SRMANet: Toward an Interpretable Neural Network with Multi-Attention Mechanism for Gearbox Fault Diagnosis

Deep neural network (DNN), with the capacity for feature inference and nonlinear mapping, has demonstrated its effectiveness in end-to-end fault diagnosis. However, the intermediate learning process of the DNN architecture is invisible, making it an uninterpretable black-box model. In this paper, a stacked residual multi-attention network (SRMANet) is proposed as a means of feature extraction of vibration signals, and visualizing the model training process, designing Squeeze-excitation residual (SE-Res) blocks to obtain additive features with minimal redundancy and sparsity. This study recommends the use of the attention fusion unit to ensure the interpretability of the model and ultimately to obtain representative features. By feeding the output gradient of the attention layer back to the original signal, the key feature components in the time domain signal can be effectively captured. Finally, the interpretability, identification accuracy and adaptability of the model under different operating conditions are verified on 12 different fault tasks in the planetary gearbox.

Fault Diagnosis Method of Circuit Breaker Based on CEEMDAN and PSO‐GSA‐SVM

Due to that the feature extraction of vibration signal to judge the mechanical fault process of circuit breaker affected by interference, a fault feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and support vector machine (SVM) is proposed. Several intrinsic modal function (IMF) components reflecting the mechanical state information of the circuit breaker during operation are extracted from the vibration signal through CEEMDAN. According to the correlation coefficient and energy distribution of each IMF, the first seven IMF components are denoised by wavelet packet soft threshold, and the sample entropy is calculated as the feature quantity. Finally, the improved gravitational search algorithm (GSA) based on particle swarm optimization (PSO) is used to optimize the SVM classifier to classify and identify the different operating states of the circuit breaker. The experimental results show that the entropy characteristics of CEEMDAN samples are insensitive to signal interference, and the PSO‐GSA‐SVM diagnosis method has a good effect on fault identification in the opening operation process of high voltage circuit breakers. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Bearing Fault Vibration Signal Feature Extraction and Recognition Method Based on EEMD Superresolution Sparse Decomposition

This paper aims at the shortcomings of the current conventional processing methods of bearing fault vibration signals in improving signal-to-noise ratio, fine feature extraction, and recognition. A feature extraction and recognition method of abnormal vibration signals based on Ensemble Empirical Mode Decomposition (EEMD) superresolution sparse decomposition is designed. First of all, the superresolution sparse decomposition method is used to refine the set of IMF components of vibration signals after EEMD decomposition. Secondly, the features of the set are extracted and their corresponding energy entropy is calculated. Thirdly, the classification and recognition are carried out. Finally, the effectiveness and feasibility of the method are verified by experiments. It has been proved that this method can better realize the denoising and fine processing aimed at abnormal vibration signals. It has certain theoretical significance and applied value.

Bearing Fault Classification using Empirical Mode Decomposition and Machine Learning Approach

Industrial machinery often breakdowns due to faults in rolling bearing. Bearing diagnosis plays a vital role in condition monitoring of machinery. Operating conditions and working environment of bearings make them prone to single or multiple faults. In this research, signals from both healthy and faulty bearings are extracted and decomposed into empirical modes. By analyzing different empirical modes from 8 derived empirical modes for healthy and faulty bearings under different fault sizes, the first mode has the most information to classify bearing condition. From the first empirical mode eight features in time domain were calculated for various bearing conditions like healthy, rolling element fault, outer and inner race fault. The feature extraction of vibration signal based on Empirical Mode Decomposition (EMD) is extensively explored and applied in diagnosis of fault in rolling bearings. This paper presents mathematical analysis for selection of valid Intrinsic Mode Functions (IMFs) of EMD. These chosen features are trained and classified using different classifiers. Among them K-star classifier is most reliable to categorize the bearing defects.

Multi-scale attention mechanism residual neural network for fault diagnosis of rolling bearings

Rolling bearing fault diagnosis is crucial to improve industrial safety and reliability. In recent years, intelligent fault diagnosis method represented by deep learning (DL) has been receiving increasing attention. In order to ameliorate the full training of the deep network, improve the model accuracy, and perfect the analysis of mechanical vibration signals with huge amount of information, a multi-scale attention mechanism residual network (MSA-ResNet) fault diagnosis method is proposed in this paper. First, an attention mechanism block is introduced to construct a new type of residual block combination. Second, a multi-scale structure is constructed by choosing an appropriate convolution kernel size. Finally, the overall framework of MSA-ResNet is constructed for efficient training and failure pattern recognition. The MSA-ResNet algorithm introduces an attention mechanism in each residual module of the residual network (ResNet), which improves the sensitivity to features. The features of different scales are obtained through the multi-scale convolution kernel, and the multi-scale feature extraction of complex nonlinear mechanical vibration signals is realized. The processing of original vibration signal rarely involves artificial interference, which is more conducive to industrial application of the proposed method. Diagnostic experiments are conducted on bearing datasets from the Case Western Reserve University (CWRU) and the Machinery Failure Prevention Technology (MFPT) to verify the effectiveness of the proposed method. The results illustrating the rolling bearing fault diagnosis method based on MSA-ResNet have advantages in multi-scale feature extraction, noise immunity, and fault classification accuracy.

Vibration-based structural damage detection via phase-based motion estimation using convolutional neural networks

Detection of structural damage is a major concern for engineers. In recent years, convolutional neural networks (CNNs) have been used for feature extraction and classification of vibration signals that reveal structural damage. Damage detection by CNNs greatly depends on high-quality learning data which are usually difficult to be obtained in actual engineering scenarios. To solve this problem, we combine phase-based motion estimation (PME) with the use of CNNs. By PME method, each pixel in a video can be regarded as a separate displacement sensor. Thus, it is possible to obtain millions of vibration signals from a single video, greatly facilitating CNN applications. We used a two-story steel structure for experimental validation. It was demonstrated that only one measured video sample obtained under each structural condition is possible to train a CNN model accurately detecting the location and severity of bolt looseness damage. This verified the outstanding performance of the proposed method.

Research on rotor system fault diagnosis method based on vibration signal feature vector transfer learning

Aiming at the common fault diagnosis problems of rotors in industrial applications. A rotor system fault diagnosis method based on vibration signal feature vector transfer learning is proposed. First, Statistical methods and wavelet packet decomposition are used for vibration signal feature extraction. Then, the ReliefF algorithm is used to evaluate the fault features and screen out the sensitive fault features set. Next, the training data and real time test data are mapped to the kernel Hilbert space using the transfer component analysis method. Finally, the weighted k-nearest neighbor method is used as the fault feature classifier for fault pattern recognition. Model training and validation using typical failure datasets of different equipment and different operating conditions. Compared with other related methods, the results indicate that the proposed method has better generalization and diagnostic accuracy. This research will promote the engineering application of intelligent fault diagnosis of rotor system.

Gear Fault Detection in a Planetary Gearbox Using Deep Belief Network

Traditional prognostics and health management (PHM) methods for fault detection require complex signal processing and manual fault feature extraction, and the accuracy is low. To address these problems, a fault diagnosis method of planetary gearbox based on deep belief networks (DBNs) is proposed. Firstly, the vibration signals of the planetary gearbox are collected and analyzed in the time domain and the frequency domain. Then, the DBN model and optimal parameters are determined to meet the task requirements. Finally, the vibration data is divided into training set and test set and input into the DBN model, which can realize the automatic feature extraction and fault recognition of vibration signals. The results show that the identification accuracy reaches 97% under five working conditions of planetary gearbox.

Variational time-domain decomposition of reciprocating machine multi-impact vibration signals

The vibration signal of reciprocating machinery is mainly formed by the superposition of multiple impact time domains related to the crankshaft angle. Impact recognition and decomposition are important for state feature extraction. The existing commonly used methods utilize the characteristics of the impact frequency domain to identify and decompose the impact through frequency-domain decomposition and reconstruction, which has some known limitations when decomposing complex and multi-impact non-stationary signals. In this study, an adaptive variational time-domain decomposition (VTDD) method was proposed for multi-impact vibration signals using the time-domain characteristics of impact waveforms. The method is mainly based on the energy concentration distribution and rapid amplitude change morphological characteristics of the impact signal. A variational model is constructed based on the minimization of high-order amplitude moments, and the alternating direction multiplier method is used to solve it iteratively. Thus, the model can adaptively find the time-domain impact waveform center and borders on both sides. Moreover, in view of the ubiquitous complex multi-impact vibration signals, an automatic solution method for the number of impacts is designed, and the optimized decomposition parameters of each impact can be adjusted adaptively. Simulated and actual mechanical multi-impact signal tests showed that the proposed method could adaptively and accurately identify the number of impacts and the center and boundary of the impact time domain in the multi-impact vibration signal. Finally, the method in this study was applied for engine valve fault vibration signal feature extraction, and the proposed method could effectively identify the additional impact caused by the fault and accurately extract the impact feature, demonstrating promising application prospects in the fault diagnosis of reciprocating machinery.

Research on feature extraction of steam turbine shafting vibration signal

Vibration signal feature extraction is the primary problem of steam turbine condition monitoring and fault diagnosis. This paper focused on the analysis and recognition of vibration signal feature extraction, carried out model innovation on the basis of the existing status, and studied new modeling and solution methods. The vibration fault signals of typical steam turbine shafting were described respectively, and the time-frequency characteristics were studied by wavelet analysis

Improved Multivariate Hierarchical Multiscale Dispersion Entropy: A New Method for Industrial Rotating Machinery Fault Diagnosis

This paper proposes an Improved Multivariate Multiscale Dispersion Entropy(IMMDE) combined with Hierarchical Entropy(HE) for vibration signal feature extraction. The traditional coarse-grained calculation is missing the relationship between neighboring sample points in the shift operation, which may lead to missing fault information. Secondly, as the scale increases, the original sequence is gradually shortened, leading to instability and inaccuracy in entropy estimation when dealing with short-term sequences. The improved coarse-grained calculation method overcomes its limitations to improve stability, and the HE method extracts deep fault frequency information from the high and low-frequency components of the multivariate signal. Then, the extracted features are dimensioned using the Max-Relevance Min-Redundancy (mRMR) to create a new set of fault features to improve diagnosis efficiency. Finally, the Support Vector Machine(SVM) determines the degree and type of fault. Experiments were conducted with three examples, the results show that IMHMDE can effectively extract the feature information according to mechanical faults’ features and improve the efficiency of fault diagnosis.

Life Prediction Method of Remanufactured Machinery Equipment Based on Vibration Signal Feature Extraction

Mechanical equipment is a key component of mechanical equipment, and its working condition is directly related to the overall performance of mechanical equipment. Accurate evaluation and prediction of the performance degradation trend of mechanical equipment is of great significance to ensure the reliability and safety of the mechanical equipment system. Based on the data of typical faulty equipment, this paper analyzes the energy characteristic parameters of mechanical equipment under different types and degrees of failure in the time domain. Using amplitude spectrum analysis, Hilbert envelope demodulation and wavelet packet decomposition method, and other vibration signal processing methods, preliminary extraction of multiple statistical feature parameters are given. Secondly, in view of the irrelevant and redundant components of multiple statistical parameters, a new method for extracting fault features of mechanical equipment based on variance value and principal component analysis is proposed. This method can effectively classify the fault status of mechanical equipment. The effectiveness of the method is verified by actual equipment signals. After that, the value extracted from the vibration signal of the double-row roller equipment is used as the degradation feature. In order to reduce the influence of irregular characteristics in the vibration signal and simplify the complexity of the vibration signal, the wavelet transform and the support vector machine model are combined, according to the degradation after decomposition. The 95% confidence interval of the predicted value is also given. The SVM model is established based on data characteristics, and single-step and multistep prediction of equipment degradation trends are carried out. The prediction result shows that, according to the mapping position formula, the distribution of equipment degradation prediction points is obtained, and a 95% confidence interval based on the distribution of the prediction points is given. Finally, on the basis of completing feature extraction, this paper applies an unsupervised feature selection method. The sensitive characteristics of life prediction and the prediction results of a single SVM model and a neural network model are compared and analyzed at the same time.

Fault Diagnosis Based on BP Neural Network Optimized by Beetle Algorithm

In the process of Wavelet Analysis, only the low-frequency signals are re-decomposed, and the high-frequency signals are no longer decomposed, resulting in a decrease in frequency resolution with increasing frequency. Therefore, in this paper, firstly, Wavelet Packet Decomposition is used for feature extraction of vibration signals, which makes up for the shortcomings of Wavelet Analysis in extracting fault features of nonlinear vibration signals, and different energy values in different frequency bands are obtained by Wavelet Packet Decomposition. The features are visualized by the K-Means clustering method, and the results show that the extracted energy features can accurately distinguish the different states of the bearing. Then a fault diagnosis model based on BP Neural Network optimized by Beetle Algorithm is proposed to identify the bearing faults. Compared with the Particle Swarm Algorithm, Beetle Algorithm can quickly find the error extreme value, which greatly reduces the training time of the model. At last, two experiments are conducted, which show that the accuracy of the model can reach more than 95%, and the model has a certain anti-interference ability.

A Fiber Vibration Signal Identification Method Based on the Combination of EWT-FE and LSTM

As the optical fiber perimeter security system is widely used in real life, how to identify the types of intrusion events in a timely and effective manner is becoming a major research hotspot. At present, in this field, various signal feature extraction algorithms are usually used to extract intrusion signal features to form feature vectors, and then machine learning algorithms are used to classify the feature vectors to achieve the role of identifying the types of intrusion events. As a common signal feature extraction algorithm, the EMD algorithm has been widely used in the feature extraction of various vibration signals, but it will have the problem of modal aliasing and affect the feature extraction effect of the signal. Therefore, EWT, VMD and other algorithms have been successively used proposed to improve modal aliasing. On the basis of fully comparing the existing algorithms, this paper proposes a fiber vibration signal identification method that decomposes the signal through the empirical wavelet transform (EWT) algorithm and then extracts the fuzzy entropy (FE) of each component, and uses LSTM for classification. The final experiment shows that the method can identify four kinds of fiber intrusion signals in time and effectively, with an average recognition accuracy rate of 97.87%, especially for flap and knock recognition rate of 100%.

A Vibration Feature Extraction Method Based on Time-Domain Dimensional Parameters and Mahalanobis Distance

To accurately describe the characteristics of a signal, the feature parameters in time domain and frequency domain are usually extracted for characterization. However, the total number of feature parameters in time domain and frequency domain exceeds twenty, and all of the feature parameters are used for feature extraction, which will result in a large amount of data processing. For the purpose of using fewer feature parameters to accurately reflect the characteristics of the vibration signal, a simple but effective vibration feature extraction method combining time-domain dimensional parameters (TDDP) and Mahalanobis distance (MD) is proposed, i.e., TDDP-MD. In this method, ten time-domain dimensional parameters are selected to extract fault features, and the distance evaluation technique based on Mahalanobis distance criterion function is also introduced to calculate the feature vector, which can be used to classify different failure types. Finally, the proposed method is applied to fault diagnosis of rolling element bearings, and experimental analysis results show that the proposed method can recognize different failure types accurately and effectively with only ten time-domain dimensional parameters and a small quantity of training samples.

Feature Extraction of Hob Vibration Signals Using Denoising Method Combining VMD and Grey Relational Analysis

Feature Extraction of Hob Vibration Signals Using Denoising Method Combining VMD and Grey Relational Analysis

Multi-scale information fusion model for feature extraction of converter transformer vibration signal

The vibration signal feature extraction model of the converter transformer has the problem of low accuracy. The main reason is that the time-related information is easily lost during training. In recent years, convolutional neural networks (CNN) have been proven to break the limits of traditional classification models. CNN has achieved remarkable results in large-scale image recognition tasks. To solve the problem of difficulty in model building and loss of time-related information, this paper proposes a multi-scale fusion feature extraction model for converter transformer vibration signal. The model includes an image generation module and a multi-scale information fusion module. The image generation module converts vibration signals into time-domain, frequency-domain, and energy feature images by calculating the Markov Transform Field and Continuous Wavelet Transform. In the multi-scale information fusion module, the attention module is introduced into the multi-parallel convolutional neural network to fuse the feature images. The proposed method can make full use of the advantages of deep learning through generating images that can represent time series from multi-scale. To evaluate the effectiveness of the model in this paper, we establish a data set based on the measured vibration signals and tested by classification experiments. The result shows the accuracy of the model state recognition in this paper is 96.15%, which is better than classic time series processing networks such as LSTM and 1D-CNN.

A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox

It is significant to boost the performance of fault diagnosis of wind turbine gearboxes. In this paper, a hybrid attention improved residual network (HA-ResNet) based method is proposed to diagnose the fault of wind turbines gearbox by highlighting the essential frequency bands of wavelet coefficients and the fault features of convolution channels. First, the paper performed wavelet packet transformation (WPT) on the raw signal and improved the ResNet by the band attention to highlight features of wavelet coefficients. Second, a fault diagnosis framework based on channel attention is designed to effectively improve the nonlinear feature extraction ability of deep convolutional networks. The proposed method is verified by a simulation dataset of the drivetrain diagnostic simulator (DDS) and the measured data from a wind farm. The results illustrate the superior performance of the HA-ResNet based fault diagnosis method for time–frequency feature extraction of vibration signals, frequency band information enhancement, and recognition accuracy improvement.