Deep Adversarial Domain Adaptation Model for Bearing Fault Diagnosis

Fault diagnosis of rolling bearings is an essential process for improving the reliability and safety of the rotating machinery. It is always a major challenge to ensure fault diagnosis accuracy in particular under severe working conditions. In this article, a deep adversarial domain adaptation (DADA...

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Vydáno v:	IEEE transactions on systems, man, and cybernetics. Systems Ročník 51; číslo 7; s. 4217 - 4226
Hlavní autoři:	Liu, Zhao-Hua, Lu, Bi-Liang, Wei, Hua-Liang, Chen, Lei, Li, Xiao-Hua, Ratsch, Matthias
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Adaptation Adversarial network Algorithms bearing Classification Data mining Data models Deep learning deep neural networks domain adaptation (DA) Domains Fault diagnosis Feature extraction Machine learning Roller bearings Rolling bearings Rotating machinery stack autoencoder (SAE) Training unsupervised learning
ISSN:	2168-2216, 2168-2232
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Abstract	Fault diagnosis of rolling bearings is an essential process for improving the reliability and safety of the rotating machinery. It is always a major challenge to ensure fault diagnosis accuracy in particular under severe working conditions. In this article, a deep adversarial domain adaptation (DADA) model is proposed for rolling bearing fault diagnosis. This model constructs an adversarial adaptation network to solve the commonly encountered problem in numerous real applications: the source domain and the target domain are inconsistent in their distribution. First, a deep stack autoencoder (DSAE) is combined with representative feature learning for dimensionality reduction, and such a combination provides an unsupervised learning method to effectively acquire fault features. Meanwhile, domain adaptation and recognition classification are implemented using a Softmax classifier to augment classification accuracy. Second, the effects of the number of hidden layers in the stack autoencoder network, the number of neurons in each hidden layer, and the hyperparameters of the proposed fault diagnosis algorithm are analyzed. Third, comprehensive analysis is performed on real data to validate the performance of the proposed method; the experimental results demonstrate that the new method outperforms the existing machine learning and deep learning methods, in terms of classification accuracy and generalization ability.
AbstractList	Fault diagnosis of rolling bearings is an essential process for improving the reliability and safety of the rotating machinery. It is always a major challenge to ensure fault diagnosis accuracy in particular under severe working conditions. In this article, a deep adversarial domain adaptation (DADA) model is proposed for rolling bearing fault diagnosis. This model constructs an adversarial adaptation network to solve the commonly encountered problem in numerous real applications: the source domain and the target domain are inconsistent in their distribution. First, a deep stack autoencoder (DSAE) is combined with representative feature learning for dimensionality reduction, and such a combination provides an unsupervised learning method to effectively acquire fault features. Meanwhile, domain adaptation and recognition classification are implemented using a Softmax classifier to augment classification accuracy. Second, the effects of the number of hidden layers in the stack autoencoder network, the number of neurons in each hidden layer, and the hyperparameters of the proposed fault diagnosis algorithm are analyzed. Third, comprehensive analysis is performed on real data to validate the performance of the proposed method; the experimental results demonstrate that the new method outperforms the existing machine learning and deep learning methods, in terms of classification accuracy and generalization ability.
Author	Liu, Zhao-Hua Lu, Bi-Liang Li, Xiao-Hua Ratsch, Matthias Chen, Lei Wei, Hua-Liang
Author_xml	– sequence: 1 givenname: Zhao-Hua orcidid: 0000-0002-6597-4741 surname: Liu fullname: Liu, Zhao-Hua email: zhaohualiu2009@hotmail.com organization: School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China – sequence: 2 givenname: Bi-Liang surname: Lu fullname: Lu, Bi-Liang email: 1197393632@qq.com organization: School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China – sequence: 3 givenname: Hua-Liang orcidid: 0000-0002-4704-7346 surname: Wei fullname: Wei, Hua-Liang email: w.hualiang@sheffield.ac.uk organization: Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, U.K – sequence: 4 givenname: Lei orcidid: 0000-0002-8000-7872 surname: Chen fullname: Chen, Lei email: chenlei@hnust.edu.cn organization: School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China – sequence: 5 givenname: Xiao-Hua orcidid: 0000-0001-8706-9964 surname: Li fullname: Li, Xiao-Hua email: lixiaohua_0227@163.com organization: School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China – sequence: 6 givenname: Matthias surname: Ratsch fullname: Ratsch, Matthias email: matthias.raetsch@reutlingen-university.de organization: Image Understanding and Interactive Robotics Group, Reutlingen University, Reutlingen, Germany
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Snippet	Fault diagnosis of rolling bearings is an essential process for improving the reliability and safety of the rotating machinery. It is always a major challenge...
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SubjectTerms	Accuracy Adaptation Adversarial network Algorithms bearing Classification Data mining Data models Deep learning deep neural networks domain adaptation (DA) Domains Fault diagnosis Feature extraction Machine learning Roller bearings Rolling bearings Rotating machinery stack autoencoder (SAE) Training unsupervised learning
Title	Deep Adversarial Domain Adaptation Model for Bearing Fault Diagnosis
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