Fault diagnosis for a class of nonlinear uncertain systems using deterministic learning approach |
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| Institution: | 1. School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, PR China;2. School of Control Science and Engineering, Shandong University, Jinan 250061, PR China;1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China;2. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, PR China;1. Dynamic Systems and Simulation Laboratory, Technical University of Crete, Chania, 73100, Greece;2. Dept. of Mathematics, National Technical University of Athens, Zografou Campus, 15780, Athens, Greece;3. Faculty of Maritime and Transportation, Ningbo University, Ningbo, China;1. Department of State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, China;2. School of Electronic Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 101408, China;3. Qiqihar Heavy CNC Equipment Corp., Ltd., Qiqihar, 161000, 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. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China;2. School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China;1. College of Science, Binzhou University, Binzhou, 256600, Shandong, China;2. Institute of Complexity Science, School of Automation, Qingdao University, Qingdao 266071, China |
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| Abstract: | In this paper, we propose a fault diagnosis (FD) approach for a class of nonlinear uncertain systems based on the deterministic learning approach (DLA). Specifically, an adaptive learning observer is constructed, in which the adaptive neural networks (NNs) are constructed to approximate the unknown system dynamics under normal and fault modes. Based on the strictly positive real (SPR) condition, the convergence of the state estimation can be guaranteed. When the system is undergoing a periodic or periodic-like (recurrent) motion, the states of the observer will also become recurrent. Thus through DLA, the partial persistent excitation (PE) condition of the associated subvectors of NNs is satisfied. By utilizing the partial (PE) condition, the uniformly completely observable (UCO) property of the identification system is analyzed and the exponential convergence condition of the identification system is derived. Under this condition, the unknown dynamics under normal and fault modes can be accurately identified along the system trajectory. And by utilizing the knowledge obtained in the identification phase, the fault can be detected in the diagnosis phase. The main attraction of this paper lies in the analytical result, which shows that the exponential convergence condition of the learning observer not only depends on the observer gain matrix, but also depends on the PE level of the regressor subvector of NN. Simulation results are included to illustrate the effectiveness of the proposed scheme. |
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