LAPSE:2019.0651
Published Article
LAPSE:2019.0651
An Intelligent Fault Diagnosis Method Using GRU Neural Network towards Sequential Data in Dynamic Processes
July 25, 2019
Intelligent fault diagnosis is a promising tool to deal with industrial big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional static intelligent diagnosis methods, however, the correlation between sequential data is neglected, and the features of raw data cannot be effectively extracted. Therefore, this paper proposes a three-stage fault diagnosis method based on a gate recurrent unit (GRU) network. The raw data is divided into several sequence units by first using a moving horizon as the input of GRU. In this way, we can intercept the sequence to get information as needed. Then, the GRU deep network is established through batch normalization (BN) algorithm to extract the dynamic feature from the sequence units effectively. Finally, the softmax regression is employed to classify faults based on dynamic features. Thus, the diagnosis result is obtained with a probabilistic explanation. Two chemical processes validate the proposed method: Tennessee Eastman (TE) benchmark process as well as para-xylene (PX) oxidation process. In the case of TE, the diagnosis results demonstrate the proposed method is superior to conventional methods. Furthermore, in the case of PX oxidation, the result shows that the proposed method also has an exceptional effect with a little historical data.
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Keywords
dynamic process, fault diagnosis, gate recurrent unit (GRU), moving horizon
Subject
Suggested Citation
Yuan J, Tian Y. An Intelligent Fault Diagnosis Method Using GRU Neural Network towards Sequential Data in Dynamic Processes. (2019). LAPSE:2019.0651
Author Affiliations
Yuan J: School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Tian Y: School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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Tian Y: School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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Journal Name
Processes
Volume
7
Issue
3
Article Number
E152
Year
2019
Publication Date
2019-03-12
Published Version
ISSN
2227-9717
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Original Submission
Other Meta
PII: pr7030152, Publication Type: Journal Article
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LAPSE:2019.0651
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External Link
doi:10.3390/pr7030152
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Version History
[v1] (Original Submission)
Jul 25, 2019
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Jul 25, 2019
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v1
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https://psecommunity.org/LAPSE:2019.0651
Original Submitter
Calvin Tsay
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