A Fault-Diagnosis Method for Railway Turnout Systems Based on Improved Autoencoder and Data Augmentation

In recent years, with the rapid increase in coverage and lines, security maintenance has become one of the top concerns with regard to railway transportation in China. As the key transportation infrastructure, the railway turnout system (RTS) plays a vital role in transportation, which will cause in...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 22; H. 23; S. 9438
Hauptverfasser:	Li, Mengyang, Hei, Xinhong, Ji, Wenjiang, Zhu, Lei, Wang, Yichuan, Qiu, Yuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 02.12.2022 MDPI
Schlagworte:	Analysis Artificial intelligence China Classification Cognitive models data augmentation Discriminant analysis Fault diagnosis Feature selection improved autoencoder Intelligence Methods Railroads railway turnout system Safety and security measures Signal processing Stations Support vector machines Technology Transportation Transportation authorities unbalanced datasets Wavelet transforms China improved autoencoder fault diagnosis data augmentation railway turnout system unbalanced datasets
ISSN:	1424-8220, 1424-8220
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Abstract	In recent years, with the rapid increase in coverage and lines, security maintenance has become one of the top concerns with regard to railway transportation in China. As the key transportation infrastructure, the railway turnout system (RTS) plays a vital role in transportation, which will cause incalculable losses when accidents occur. The traditional fault-diagnosis and maintenance methods of the RTS are no longer applicable to the growing amount of data, so intelligent fault diagnosis has become a research hotspot. However, the key challenge of RTS intelligent fault diagnosis is to effectively extract the deep features in the signal and accurately identify failure modes in the face of unbalanced datasets. To solve the above two problems, this paper focuses on unbalanced data and proposes a fault-diagnosis method based on an improved autoencoder and data augmentation, which realizes deep feature extraction and fault identification of unbalanced data. An improved autoencoder is proposed to smooth the noise and extract the deep features to overcome the noise fluctuation caused by the physical characteristics of the data. Then, synthetic minority oversampling technology (SMOTE) is utilized to effectively expand the fault types and solve the problem of unbalanced datasets. Furthermore, the health state is identified by the Softmax regression model that is trained with the balanced characteristics data, which improves the diagnosis precision and generalization ability. Finally, different experiments are conducted on a real dataset based on a railway station in China, and the average diagnostic accuracy reaches 99.13% superior to other methods, which indicates the effectiveness and feasibility of the proposed method.
AbstractList	In recent years, with the rapid increase in coverage and lines, security maintenance has become one of the top concerns with regard to railway transportation in China. As the key transportation infrastructure, the railway turnout system (RTS) plays a vital role in transportation, which will cause incalculable losses when accidents occur. The traditional fault-diagnosis and maintenance methods of the RTS are no longer applicable to the growing amount of data, so intelligent fault diagnosis has become a research hotspot. However, the key challenge of RTS intelligent fault diagnosis is to effectively extract the deep features in the signal and accurately identify failure modes in the face of unbalanced datasets. To solve the above two problems, this paper focuses on unbalanced data and proposes a fault-diagnosis method based on an improved autoencoder and data augmentation, which realizes deep feature extraction and fault identification of unbalanced data. An improved autoencoder is proposed to smooth the noise and extract the deep features to overcome the noise fluctuation caused by the physical characteristics of the data. Then, synthetic minority oversampling technology (SMOTE) is utilized to effectively expand the fault types and solve the problem of unbalanced datasets. Furthermore, the health state is identified by the Softmax regression model that is trained with the balanced characteristics data, which improves the diagnosis precision and generalization ability. Finally, different experiments are conducted on a real dataset based on a railway station in China, and the average diagnostic accuracy reaches 99.13% superior to other methods, which indicates the effectiveness and feasibility of the proposed method. In recent years, with the rapid increase in coverage and lines, security maintenance has become one of the top concerns with regard to railway transportation in China. As the key transportation infrastructure, the railway turnout system (RTS) plays a vital role in transportation, which will cause incalculable losses when accidents occur. The traditional fault-diagnosis and maintenance methods of the RTS are no longer applicable to the growing amount of data, so intelligent fault diagnosis has become a research hotspot. However, the key challenge of RTS intelligent fault diagnosis is to effectively extract the deep features in the signal and accurately identify failure modes in the face of unbalanced datasets. To solve the above two problems, this paper focuses on unbalanced data and proposes a fault-diagnosis method based on an improved autoencoder and data augmentation, which realizes deep feature extraction and fault identification of unbalanced data. An improved autoencoder is proposed to smooth the noise and extract the deep features to overcome the noise fluctuation caused by the physical characteristics of the data. Then, synthetic minority oversampling technology (SMOTE) is utilized to effectively expand the fault types and solve the problem of unbalanced datasets. Furthermore, the health state is identified by the Softmax regression model that is trained with the balanced characteristics data, which improves the diagnosis precision and generalization ability. Finally, different experiments are conducted on a real dataset based on a railway station in China, and the average diagnostic accuracy reaches 99.13% superior to other methods, which indicates the effectiveness and feasibility of the proposed method.In recent years, with the rapid increase in coverage and lines, security maintenance has become one of the top concerns with regard to railway transportation in China. As the key transportation infrastructure, the railway turnout system (RTS) plays a vital role in transportation, which will cause incalculable losses when accidents occur. The traditional fault-diagnosis and maintenance methods of the RTS are no longer applicable to the growing amount of data, so intelligent fault diagnosis has become a research hotspot. However, the key challenge of RTS intelligent fault diagnosis is to effectively extract the deep features in the signal and accurately identify failure modes in the face of unbalanced datasets. To solve the above two problems, this paper focuses on unbalanced data and proposes a fault-diagnosis method based on an improved autoencoder and data augmentation, which realizes deep feature extraction and fault identification of unbalanced data. An improved autoencoder is proposed to smooth the noise and extract the deep features to overcome the noise fluctuation caused by the physical characteristics of the data. Then, synthetic minority oversampling technology (SMOTE) is utilized to effectively expand the fault types and solve the problem of unbalanced datasets. Furthermore, the health state is identified by the Softmax regression model that is trained with the balanced characteristics data, which improves the diagnosis precision and generalization ability. Finally, different experiments are conducted on a real dataset based on a railway station in China, and the average diagnostic accuracy reaches 99.13% superior to other methods, which indicates the effectiveness and feasibility of the proposed method.
Audience	Academic
Author	Zhu, Lei Ji, Wenjiang Wang, Yichuan Li, Mengyang Hei, Xinhong Qiu, Yuan
AuthorAffiliation	School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
AuthorAffiliation_xml	– name: School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
Author_xml	– sequence: 1 givenname: Mengyang surname: Li fullname: Li, Mengyang – sequence: 2 givenname: Xinhong orcidid: 0000-0002-6394-0492 surname: Hei fullname: Hei, Xinhong – sequence: 3 givenname: Wenjiang orcidid: 0000-0001-5502-1579 surname: Ji fullname: Ji, Wenjiang – sequence: 4 givenname: Lei orcidid: 0000-0001-5925-5743 surname: Zhu fullname: Zhu, Lei – sequence: 5 givenname: Yichuan orcidid: 0000-0001-6575-1954 surname: Wang fullname: Wang, Yichuan – sequence: 6 givenname: Yuan surname: Qiu fullname: Qiu, Yuan
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Snippet	In recent years, with the rapid increase in coverage and lines, security maintenance has become one of the top concerns with regard to railway transportation...
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StartPage	9438
SubjectTerms	Analysis Artificial intelligence China Classification Cognitive models data augmentation Discriminant analysis Fault diagnosis Feature selection improved autoencoder Intelligence Methods Railroads railway turnout system Safety and security measures Signal processing Stations Support vector machines Technology Transportation Transportation authorities unbalanced datasets Wavelet transforms
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Title	A Fault-Diagnosis Method for Railway Turnout Systems Based on Improved Autoencoder and Data Augmentation
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Volume	22
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