scholarly journals Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Zhe Tong ◽  
Wei Li ◽  
Bo Zhang ◽  
Meng Zhang
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Nearest Neighbor ◽  
Domain Adaptation ◽  
Vibration Signal ◽  
Feature Representation ◽  
Training Data ◽  
Fast Fourier Transformation ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Bearing failure is the most common failure mode in rotating machinery and can result in large financial losses or even casualties. However, complex structures around bearing and actual variable working conditions can lead to large distribution difference of vibration signal between a training set and a test set, which causes the accuracy-dropping problem of fault diagnosis. Thus, how to improve efficiently the performance of bearing fault diagnosis under different working conditions is always a primary challenge. In this paper, a novel bearing fault diagnosis under different working conditions method is proposed based on domain adaptation using transferable features(DATF). The datasets of normal bearing and faulty bearings are obtained through the fast Fourier transformation (FFT) of raw vibration signals under different motor speeds and load conditions. Then we reduce marginal and conditional distributions simultaneously across domains based on maximum mean discrepancy (MMD) in feature space by refining pseudo test labels, which can be obtained by the nearest-neighbor (NN) classifier built on training data, and then a robust transferable feature representation for training and test domains is achieved after several iterations. With the help of the NN classifier trained on transferable features, bearing fault categories are identified accurately in final. Extensive experiment results show that the proposed method under different working conditions can identify the bearing faults accurately and outperforms obviously competitive approaches.

scholarly journals Rolling Bearing Fault Diagnosis Based on Domain Adaptation and Preferred Feature Selection under Variable Working Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-27
Author(s):  
Xiao Yu ◽  
Wei Chen ◽  
Chuanlong Wu ◽  
Enjie Ding ◽  
Yuanyuan Tian ◽  
...  
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Domain Adaptation ◽  
Rolling Bearing ◽  
Machine Learning Techniques ◽  
Bearing Fault ◽  
Method Transfer ◽  
Bearing Fault Diagnosis ◽  
Manifold Structure

In real industrial scenarios, with the use of conventional machine learning techniques, data-driven diagnosis models have a limitation that it is difficult to achieve the desirable fault diagnosis performance, and the reason is that the training and testing datasets are assumed to have the same feature distributions. To address this problem, a novel bearing fault diagnosis framework based on domain adaptation and preferred feature selection is proposed, in that the model trained by the labeled data collected from a working condition can be applied to diagnose a new but similar target data collected from other working conditions. In this framework, an improved domain adaptation method, transfer component analysis with preserving local manifold structure (TCAPLMS), is proposed to reduce the differences in the data distributions between different domain datasets and, at the same time, take the label information of feature dataset and the local manifold structure of feature data into consideration. Furthermore, preferred feature selection by fault sensitivity and feature correlation (PSFFC) is embedded into this framework for selecting features which are more beneficial to fault pattern recognition and reduce the redundancy of feature set. Finally, vibration datasets collected from two test platforms are used for experimental analysis. The experimental results validate that the proposed method can obviously improve diagnosis accuracy and has significant potential benefits towards actual industrial scenarios.

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 A New Deep Convolutional Domain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Yongchao Zhang ◽  
Zhaohui Ren ◽  
Shihua Zhou
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Domain Adaptation ◽  
Classification Error ◽  
Mechanical Equipment ◽  
Target Domain ◽  
Source Domain ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Cross Domain

Effective fault diagnosis methods can ensure the safe and reliable operation of the machines. In recent years, deep learning technology has been applied to diagnose various mechanical equipment faults. However, in real industries, the data distribution under different working conditions is often different, which leads to serious degradation of diagnostic performance. In order to solve the issue, this study proposes a new deep convolutional domain adaptation network (DCDAN) method for bearing fault diagnosis. This method implements cross-domain fault diagnosis by using the labeled source domain data and the unlabeled target domain data as training data. In DCDAN, firstly, a convolutional neural network is applied to extract features of source domain data and target domain data. Then, the domain distribution discrepancy is reduced through minimizing probability distribution distance of multiple kernel maximum mean discrepancies (MK-MMD) and maximizing the domain recognition error of domain classifier. Finally, the source domain classification error is minimized. Extensive experiments on two rolling bearing datasets verify that the proposed method can implement accurate cross-domain fault diagnosis under different working conditions. The study may provide a promising tool for bearing fault diagnosis under different working conditions.

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