Design teacher and supervised dual stacked auto-encoders for quality-relevant fault detection in industrial process |
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| Affiliation: | 1. Bohai University, Jinzhou 121013, China;2. Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Duisburg 47057, Germany;3. Key Laboratory for Advanced Control of Iron and Steel Process, University of Science and Technology of Beijing, Beijing 100083, China;4. School of Automation and Electrical Engineering, University of Science and Technology of Beijing, Beijing 100083, China;5. Bohai University, Jinzhou 121013, China;1. Shunde Graduate School of University of Science and Technology Beijing, Foshan 528399, China;2. Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;3. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China |
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| Abstract: | Current fault detection methods based on deep neural networks only consider process information and ignore quality indicators. In order to obtain features representing both process variables and quality indicators efficiently, this paper designs teacher and supervise dual stacked auto-encoder (TSSAE) for quality-relevant fault detection in industrial process which separates the feature extraction and model construction. To separate the feature extraction and model construction, a mixing stacked auto-encoder which consists of a nonlinear encoder and a linear decoder is designed to extract features of process variables and quality indicators. Another encoder is supervised by the extracted features and further predict the process variables and quality indicators only from process variables. Then quality-relevant, quality-irrelevant and residual subspaces are constructed in a linear way and fault detection is implemented in these subspaces based on Euclidean distance and kernel density estimation. Finally, the effectiveness of TSSAE is evaluated by a numerical example and the Tennessee-Eastman process. |
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| Keywords: | Deep neural network Fault detection Quality-relevant Stacked auto-encoder |
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