scholarly journals Motor Fault Diagnosis Algorithm Based on Wavelet and Attention Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yong Yan ◽  
Qiang Liu ◽  
Xiao qin Gao
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Attention Mechanism ◽  
Time Dimension ◽  
High Frequency Signal ◽  
Diagnosis Accuracy ◽  
Diagnosis Algorithm ◽  
Motor Vibration

In order to improve the maintenance efficiency of the motor and realize the real-time fault diagnosis function of the motor, a motor fault diagnosis algorithm based on wavelet and attention mechanism is proposed. Firstly, the motor vibration signal is decomposed by wavelet transform, and the high-frequency signal is denoised to improve the signal-to-noise ratio. Secondly, the frequency band and time dimension after wavelet decomposition are taken as input data, the convolution neural network is used to fuse the frequency band features of data, and the bidirectional gated loop unit is used to fuse the time series features. Then, the attention mechanism is used to adaptively integrate the features of different time points. Finally, motor fault diagnosis and prediction are realized by classifier recognition. Experimental results show that, compared with the existing deep learning fault diagnosis model, this method has higher diagnosis accuracy and can accurately diagnose the running state of the motor.

The Wavelet Analysis and Processing of Weak Vibration Signal

2011 ◽  
Vol 189-193 ◽  
pp. 1426-1431
Author(s):  
Ze Ning Xu ◽  
Hong Yu Liu ◽  
Yong Guo Zhang
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Basic Feature ◽  
Wavelet Function ◽  
Fault Analysis ◽  
Bearing Fault Diagnosis ◽  
Gear Fault ◽  
Vibratory Signals

Signal measuring is an important link in machine fault diagnosis. Accurate and reliable fault signals can be achieved by reasonable signal measuring. When the distance between sensor and measuring gear or bearing is comparatively far, the collected signals became weak and disturbed by other vibratory signals in equipments on bearing and gear fault analysis. Useful signals often were submerged in powerful noise, so caused difficult in extracting fault feature. In this paper, according to the feature of vibratory signals in machine test, wavelet analysis basic theory was applied on researching basic feature of wavelet analysis. By selecting suitable wavelet function and applying wavelet elimination noise technology the signal to noise ratio of signal was raised, thus the vibratory impact component can be measured in weak signals. Finally, wavelet analysis was applied on bearing fault diagnosis.

scholarly journals Planetary Gearbox Fault Diagnosis Using Envelope Manifold Demodulation

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Weigang Wen ◽  
Robert X. Gao ◽  
Weidong Cheng
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
Manifold Learning ◽  
Planetary Gear ◽  
Vibration Signal ◽  
Planetary Gearbox ◽  
Characteristic Extraction ◽  
Gear Fault ◽  
Planet Gear ◽  
Fault Characteristic

The important issue in planetary gear fault diagnosis is to extract the dependable fault characteristics from the noisy vibration signal of planetary gearbox. To address this critical problem, an envelope manifold demodulation method is proposed for planetary gear fault detection in the paper. This method combines complex wavelet, manifold learning, and frequency spectrogram to implement planetary gear fault characteristic extraction. The vibration signal of planetary gear is demodulated by wavelet enveloping. The envelope energy is adopted as an indicator to select meshing frequency band. Manifold learning is utilized to reduce the effect of noise within meshing frequency band. The fault characteristic frequency of the planetary gear is shown by spectrogram. The planetary gearbox model and test rig are established and experiments with planet gear faults are conducted for verification. All results of experiment analysis demonstrate its effectiveness and reliability.

scholarly journals An Intelligent Quadrotor Fault Diagnosis Method Based on Novel Deep Residual Shrinkage Network

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 133
Author(s):  
Pu Yang ◽  
Huilin Geng ◽  
Chenwan Wen ◽  
Peng Liu
Keyword(s):  
Fault Diagnosis ◽  
Window Method ◽  
Signal Features ◽  
Fault Features ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Sliding Window Method ◽  
Diagnosis Algorithm ◽  
Soft Threshold ◽  
Fitting Ability

In this paper, a fault diagnosis algorithm named improved one-dimensional deep residual shrinkage network with a wide convolutional layer (1D-WIDRSN) is proposed for quadrotor propellers with minor damage, which can effectively identify the fault classes of quadrotor under interference information, and without additional denoising procedures. In a word, that fault diagnosis algorithm can locate and diagnose the early minor faults of the quadrotor based on the flight data, so that the quadrotor can be repaired before serious faults occur, so as to prolong the service life of quadrotor. First, the sliding window method is used to expand the number of samples. Then, a novel progressive semi-soft threshold is proposed to replace the soft threshold in the deep residual shrinkage network (DRSN), so the noise of signal features can be eliminated more effectively. Finally, based on the deep residual shrinkage network, the wide convolution layer and DroupBlock method are introduced to further enhance the anti-noise and over-fitting ability of the model, thus the model can effectively extract fault features and classify faults. Experimental results show that 1D-WIDRSN applied to the minimal fault diagnosis model of quadrotor propellers can accurately identify the fault category in the interference information, and the diagnosis accuracy is over 98%.

Simultaneous Fault Diagnosis of Main Retarder Using Improved Paired Relevance Vector Machine Based on Multi-Kernel Learning

2014 ◽  
Vol 614 ◽  
pp. 339-344
Author(s):  
Qing Ye ◽  
Hao Pan
Keyword(s):  
Fault Diagnosis ◽  
Cross Validation ◽  
Computational Cost ◽  
Vibration Signal ◽  
Relevance Vector Machine ◽  
Kernel Learning ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Fault Diagnosis System ◽  
Low Computational Cost

According to the practical requirement of auto manufacturer, excellent fault diagnosis system aiming at simultaneous fault is indispensable for main retarder of automobile. This paper proposes a novel diagnosis method which employs wavelet package transform and sample entropy to achieve feature extraction, later utilize relevance vector machine to construct a set of paired classifiers. Considering that features extracted from vibration signal are multiple and heterogeneous, we combine multi-kernel learning and relevance vector machine together and optimize kernel function parameters by using incremental learning, cross validation and genetic algorithm. Comparing with SVM and PNN, the experiment results verify high diagnosis accuracy and low computational cost of the proposed method.

scholarly journals Fault Diagnosis of a Reciprocating Compressor Air Valve Based on Deep Learning

2020 ◽  
Vol 10 (18) ◽  
pp. 6596
Author(s):  
Shungen Xiao ◽  
Ang Nie ◽  
Zexiong Zhang ◽  
Shulin Liu ◽  
Mengmeng Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Complex Systems ◽  
Single Channel ◽  
Vibration Signal ◽  
Extraction Methods ◽  
Reciprocating Compressor ◽  
Deep Convolutional Neural Networks ◽  
Phase Signal ◽  
Diagnosis Accuracy

With the development of machine learning in recent years, the application of machine learning to machine fault diagnosis has become increasingly popular. Applying traditional feature extraction methods for complex systems will weaken the characterization capacity of features, which are not conducive to subsequent classification work. A reciprocating compressor is a complex system. In order to improve the fault diagnosis accuracy of complex systems, this paper does not use traditional fault diagnosis methods and applies deep convolutional neural networks (CNNs) to process this nonlinear and non-stationary fault signal. The valve fault data is obtained from the reciprocating compressor test bench of the Daqing Natural Gas Company. Firstly, the single-channel vibration signal is collected on the reciprocating compressor and the one-dimensional CNN (1-D CNN) is used for fault diagnosis and compared with the traditional model to verify the effectiveness of the 1-D CNN. Next, the collected eight channels signals (three channels of vibration signals, four channels of pressure signals, one channel key phase signal) are applied by 1-D CNN and 2-D CNN for fault diagnosis to verify the CNN that it is still suitable for multi-channel signal processing. Finally, further study on the influence of the input of different channel signal combinations on the model diagnosis accuracy is carried out. Experiments show that the seven-channel signal (three-channel vibration signal, four-channel pressure signal) with the key phase signal removed has the highest diagnostic accuracy in the 2-D CNN. Therefore, proper deletion of useless channels can not only speed up network operations but also improve diagnosis accuracy.

scholarly journals Application of Xgboost Feature Extraction in Fault Diagnosis of Rolling Bearing

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Xingang WANG ◽  
Chao WANG
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Time Domain ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Support Vector ◽  
Time Frequency ◽  
Diagnosis Accuracy

Due to the difficulty that excessive feature dimension in fault diagnosis of rolling bearing will lead to the decrease of classification accuracy, a fault diagnosis method based on Xgboost algorithm feature extraction is proposed. When the Xgboost algorithm classifies features, it generates an order of importance of the input features. The time domain features were extracted from the vibration signal of the rolling bearing, the time-frequency features were formed by the singular value of the modal components that were decomposed by the variational mode decomposition. Firstly, the extracted time domain and time-frequency domain features were input into the support vector machine respectively to observe the fault diagnosis accuracy. Then, Xgboost algorithm was used to rank the importance of features and got the accuracy of fault diagnosis. Finally, important features were extracted and the extracted features were input into the support vector machine to observe the fault diagnosis accuracy. The result shows that the fault diagnosis accuracy of rolling bearing is improved after important feature extraction in time domain and time-frequency domain by Xgboost.

scholarly journals Rolling Bearing Fault Vibration Signal Denoising Based on Adaptive Morphological Wavelet Perona–Malik Filter Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hao Li ◽  
Yifan Tan ◽  
Yun Pu
Keyword(s):  
Fault Diagnosis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Haar Wavelet ◽  
Signal Denoising ◽  
Second Derivative ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Noise Point

This paper proposes an adaptive Perona–Malik filtering algorithm based on the morphological Haar wavelet, which is used for vibration signal denoising in rolling bearing fault diagnosis with strong noise. First, the morphological Haar wavelet operator is utilized to presmooth the noisy signal, and the gradient of the presmooth signal is estimated. Second, considering the uncertainty of gradient at the strong noise point, a strong noise point recognition operator is constructed to adaptively identify the strong noise point. Third, the two-step gradient average value of the strong noise point in the same direction is used to substitute, and the second derivative is introduced into the diffusion coefficient. Finally, diffusion filtering is performed based on the improved Perona–Malik model. The simulation experiment result indicated that not only the algorithm can denoise effectively, but also the average gradient and second derivative in the same direction can effectively suppress the back diffusion of strong noise points to improve the denoising signal-to-noise ratio. The experimental results of rolling bearing show that the algorithm can adaptively filter out strong noise points and keep the information of peak in the signal well, which can improve the accuracy of rolling bearing fault diagnosis.

scholarly journals Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short-Term Memory

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Dazhang You ◽  
Linbo Chen ◽  
Fei Liu ◽  
YePeng Zhang ◽  
Wei Shang ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Vibration Signal ◽  
Short Term ◽  
Term Memory ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Accuracy ◽  
Long Short Term Memory

The traditional bearing fault diagnosis methods have complex operation processes and poor generalization ability, while the diagnosis accuracy of the existing intelligent diagnosis methods needs to be further improved. Therefore, a novel fault diagnosis approach named CNN-BLSTM for bearing is presented based on convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) in this paper. This method directly takes the collected one-dimensional raw vibration signal as input and adaptively extracts the feature information through CNN. Then, the BLSTM is used to fuse the extracted features to acquire the failure information sufficiently and prevent the model from overfitting. Finally, two different experimental datasets are used to verify the effectiveness of the method. The experimental results show that the proposed CNN-BLSTM model can accurately diagnose the fault category of bearings. It has the advantages of rapidity, stability, antinoise, and strong generalization.

Frequency Band Energy Statistics Method and the Applications in Monitoring the Work Conditions for Mechanical Equipments

2010 ◽  
Vol 29-32 ◽  
pp. 78-83
Author(s):  
Yun Long Yuan ◽  
Chao Yong Yuan ◽  
Chao Zhen Yang
Keyword(s):  
Fault Diagnosis ◽  
Energy Distribution ◽  
Frequency Band ◽  
Present Method ◽  
Vibration Signal ◽  
Work Conditions ◽  
Current Work ◽  
Vibration Signals ◽  
Band Energy ◽  
Frequency Band Energy

A new manner, named frequency band energy statistics method, has been proposed for the analysis of the spectra from the vibration signals. The recorded vibration signals were first divided into multi-segments. Then each segment was calculated via the FFT transformation and 1/3 octave spectrum to obtained the characteristics of energy distribution, by making the histogram maps of the obtained features of the frequency energies. Consequently, we can monitor the work conditions and fault diagnosis for the mechanical equipments by the compared analysis of the corresponding histogram maps of equipments with normal and abnormal work conditions. The results show that present method exhibits a very strong sensitivity to the changes of vibration signal, leading to the fine detection of the minor changes form the equipment work conditions. Current work might provide a novel and facile method for definitely monitoring the work conditions and fault diagnosis of the mechanical equipments.

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