scholarly journals Fault Diagnosis of Subway Traction Motor Bearing Based on Information Fusion under Variable Working Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yanwei Xu ◽  
Weiwei Cai ◽  
Tancheng Xie
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Information Fusion ◽  
Working Condition ◽  
Rolling Bearing ◽  
Traction Motor ◽  
Bearing Fault ◽  
Diagnosis Method ◽  
Motor Bearing

Under the variable working condition, the fault signal of the rolling bearing contains rich characteristic information. In view of the problem that the traditional fault diagnosis method of the rolling bearing depends on the prior knowledge and expert experience too much and the low recognition rate of some faults with the single signal, one method of rolling bearing fault diagnosis based on information fusion under the variable working condition is proposed. Firstly, one test and multi-information acquisition system of the rolling bearing is built. Secondly, the metro traction motor bearing nu216 is selected as the research object, and to prefabricate the defects, the data of acoustic emission and vibration acceleration signals during the test of the bearing is acquired. Then, the original signal is processed and extracted by the wavelet packet decomposition, and the normalized feature information is fused by the convolution neural network. Finally, the two-dimensional convolution neural network model is established to diagnose the bearing fault of the metro traction motor under different conditions. The test results show that the intelligent fault diagnosis method of the subway traction motor bearing based on information fusion under variable working conditions can accurately identify the fault type of the bearing, while the load and speed change. When the neural network training set and the test set cover the same working conditions, the accuracy can reach 100%.

Fault Diagnosis Method for the Rolling Bearing Based on Information Fusion and BP Neural Network

2012 ◽  
Vol 538-541 ◽  
pp. 1956-1961 ◽  
Author(s):  
Jin Min Zhang ◽  
Yin Hua Huang ◽  
Si Ming Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Back Propagation Neural Network ◽  
Weighted Fusion ◽  
Diagnosis Method

Abstract. In order to diagnose the fault of rolling bearing by the vibration signal, a new method of fault diagnosis based on weighted fusion and BP (Back Propagation) neural network was put forward. At first, the vibration signal from the sensors was wave filtered through the method of correlation function, then the fused signal was obtained by the classical adaptive weighted fusion method, the multi-type characteristics parameters was to be as a neural network input. Finally, the fault diagnosis of rolling bearing was realized by the BP neural network, and the results show that the multi-sensor information fusion fault diagnosis method can be proved effectively to achieve the fault diagnosis of rolling bearing.

scholarly journals Intelligent Diagnosis of Subway Traction Motor Bearing Fault Based on Improved Stacked Denoising Autoencoder

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yanwei Xu ◽  
Chen Li ◽  
Tancheng Xie
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Vibration Signal ◽  
Variable Load ◽  
Denoising Autoencoder ◽  
Traction Motor ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Feature Values ◽  
Motor Bearing

Aiming at the problem that the complex working conditions affect the effect of manual feature extraction in bearing fault diagnosis of metro traction motor, a fault diagnosis method of metro traction motor bearing based on improved stacked denoising autoencoder (SDAE) is proposed. This method extracts fault features directly from the original vibration signal through deep learning, reduces the dependence on signal processing technology and diagnosis experience, and solves the problem of unsatisfactory effect of extracting feature values under complex working conditions. The effect of the improved SDAE network structure on the accuracy of bearing fault diagnosis is studied through experiments, and the best network parameters are selected. The test results show that the proposed method can well extract the deep features of the fault under the condition of variable speed and variable load; when using data sets with complex working conditions, the classification accuracy of the proposed method is better than that of many traditional fault diagnosis methods.

scholarly journals Rolling Bearing Fault Diagnosis based on Residual Neural Network

2020 ◽  
Vol 2 (4) ◽  
pp. 89
Author(s):  
Haopeng Liang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Diagnosis Method ◽  
Hilbert Envelope ◽  
Envelope Spectrum ◽  
Residual Block

<div class="Section0"><div>Because rolling bearings have been working in an environment with complex and variable working conditions and large noise interference for a long time, the bearing fault diagnosis method has a poor diagnostic effect under variable working conditions. To solve this problem, we propose a residual neural network based on the diagnosis method of rolling bearing fault. The proposed method takes rolling bearing time-domain signal data as input. Because bearing signals have strong time-varying properties, we construct a multi-scale residual block that can not only learn features at different levels, but also expand the width and depth of the residual neural network. We use the advantages of the dilated convolution to expand the receptive field, replace part of the ordinary convolution in the multi-scale residual block with the dilated convolution, and design a multi-scale hollow residual block. The advantage is that the method is made by expanding the receptive field. It has a strong feature learning ability and can learn better features under limited data. Finally, we add a Dropout layer to discard a certain proportion of neurons after the fully connected layer, which can effectively avoid the negative impact of overfitting, and use Case Western Reserve University bearing dataset, the simulation experiment, and the SVM + EMD + Hilbert envelope spectrum, BPNN + EMD + Hilbert envelope spectrum and Resnet three ways of comparative analysis, the results show that the method under the variable condition of the fault diagnosis of rolling bearing has higher diagnosis accuracy, stronger noise resistance, and generalization ability.</div><p> </p></div><p> </p>

A rolling bearing fault diagnosis method using novel lightweight neural network

2021 ◽  
Author(s):  
Deqiang He ◽  
Chenyu Liu ◽  
Yanjun Chen ◽  
Zhenzhen Jin ◽  
Xianwang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

scholarly journals Bearing Fault Diagnosis Method Based on EEMD and LSTM

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Ping Zou ◽  
Baocun Hou ◽  
Jiang Lei ◽  
Zhenji Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Small Sample Size ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Small Sample ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Signal Preprocessing ◽  
Diagnosis Method

The condition monitoring and fault detection of rolling bearing are of great significance to ensure the safe and reliable operation of rotating machinery system.In the past few years, deep neural network (DNN) has been recognized as an effective tool to detect rolling bearing faults. However, It is too complex to directly feed the original vibration signal to the DNN neural network, and the accuracy of fault identification is not high. By using the signal preprocessing technology, the original signal can be effectively removed and preprocessed without losing the key diagnosis information. In this paper, a new EEMD-LSTM bearing fault diagnosis method is proposed, which combines the signal preprocessing technology with the EEMD method that can get clear fault feature signals, and LSTM technology to extract fault features automatically that improves the efficiency of fault feature extraction. In the case of small sample size, this method can significantly improve the accuracy of fault diagnosis.

scholarly journals Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Bo Zhou ◽  
Yujie Cheng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Image Recognition ◽  
Working Conditions ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Kernel Principal Component Analysis ◽  
Two Dimensional ◽  
Rolling Bearings ◽  
Diagnosis Method

Rolling bearing faults often lead to electromechanical system failure due to its high speed and complex working conditions. Recently, a large amount of fault diagnosis studies for rolling bearing based on vibration data has been reported. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper proposes a fault diagnosis method based on image recognition for rolling bearings to realize fault classification under variable working conditions. The proposed method includes the following steps. First, the vibration signal data are transformed into a two-dimensional image based on recurrence plot (RP) technique. Next, a popular feature extraction method which has been widely used in the image field, scale invariant feature transform (SIFT), is employed to extract fault features from the two-dimensional RP and subsequently generate a 128-dimensional feature vector. Third, due to the redundancy of the high-dimensional feature, kernel principal component analysis is utilized to reduce the feature dimensionality. Finally, a neural network classifier trained by probabilistic neural network is used to perform fault diagnosis. Verification experiment results demonstrate the effectiveness of the proposed fault diagnosis method for rolling bearings under variable conditions, thereby providing a promising approach to fault diagnosis for rolling bearings.

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