Nonlinear process monitoring based on generic reconstruction-based auto-associative neural network |
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| Institution: | 1. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, 210044, China;2. College of Automation, Chongqing University, Chongqing 400044, China;1. Key Laboratory of Intelligent Analysis and Decision on Complex Systems, School of Science, Chongqing University of Posts and Telecommunications, Chongqing, PR China;2. Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing, College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, PR China;3. Department of Complexity Science, Potsdam Institute for Climate Impact Research, Potsdam, Germany;4. Institute of Physics, Humboldt University of Berlin, Berlin, Germany;1. Engineering Research Center of Internet of Things Technology and Applications (Ministry of Education), Jiangnan University, Wuxi, Jiangsu 214122, China;2. Department of Electrical Engineering, Yeungnam University, Kyongsan, Republic of Korea;1. School of Mathematics and Statistics, Key Laboratory of Applied Statistics of MOE, Northeast Normal University, Changchun 130024, Jilin Province, China;2. College of Science, China University of Petroleum, Qingdao 266580, Shandong Province, China;3. Logistics Industry Economy and Intelligent Logistics Laboratory, Jilin University of Finance and Economics, Changchun 130117, Jilin Province, China |
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| Abstract: | A significant concern with statistical fault diagnosis is the large number of false alarms caused by the smearing effect. Although the reconstruction-based approach effectively solves this problem, most of them only focus on linear rather than nonlinear systems. In the present work, a generic reconstruction-based auto-associative neural network (GRBAANN) is proposed that uses the reconstruction-based approach to isolate simple and complex faults for nonlinear systems. Nevertheless, in GRBAANN, it is challenging to acquire a trivial solution for the reconstruction-based index, which is equivalent to a complex vector fixed-point problem. In this regard, the Steffensen method is employed to deal with this problem with an accelerated iterative process, which is appropriate for both single and multiple variable faults. The variable selection procedure is time-consuming but imperative for reconstruction-based approaches, with no exception to the proposed method. In order to ensure the real-time diagnosis for large-scale systems, the Sequential floating forward selection method with memory is proposed to minimize the computation time of the variable selection procedure. The effectiveness of the proposed GRBAANN scheme is illustrated through a validation example and an industrial example. Comparisons with the state-of-art methods are also presented. |
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