A process monitoring and fault isolation framework based on variational autoencoders and branch and bound method |
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| Institution: | 1. 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, PR China;2. Department of Mathematics and Theories, Peng Cheng Laboratory, No.2, Xingke 1st Street, Nanshan, Shenzhen, China;3. AVIC Xi’an Aviation Brake Technology Co., Ltd., Xi’an, 710065, China;1. State Key Laboratory of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, China;2. Department of Chemical Engineering, Auburn University, Auburn Alabama, 36849, USA;3. Beijing Key Laboratory of Industrial Big Data System and Application, Tsinghua University, Beijing, China |
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| Abstract: | Nonlinear characteristic widely exists in industrial processes. Many approaches based on kernel methods and machine learning have been developed for nonlinear process monitoring. However, the fault isolation for nonlinear processes has rarely been studied in previous works. In this paper, a process monitoring and fault isolation framework is proposed for nonlinear processes using variational autoencoder (VAE) model. First, based on the probability graph model of VAE, a uniform monitoring index can be calculated by the probability density of observation variables. Then, the fault variables are estimated with normal variables by a missing value estimation method. The optimal fault variable set can be searched by branch and bound (BAB) algorithm. The proposed method can resolve the ”smearing effects” problem existing in traditional fault isolation methods. Finally, a numerical case and a hot strip mill process case are used to verified the proposed method. |
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