Fault prognostic of electronics based on optimal multi-order particle filter |
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| Affiliation: | 1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;2. Anhui University of Science and Technology, Huainan 230012, China;1. Robert Bosch GmbH, Reliability Modeling and System Optimization (AE/EDT3) Reutlingen, 72703, Germany;2. Fraunhofer-Institut für Betriebsfestigkeit und Systemzuverlässigkeit LBF, Darmstadt 64289, Germany;3. Mechanical Engineering Department, University of Maryland, College Park, MD 20742, USA;1. School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore;2. CNES, French Space Agency, 18 Avenue Edouard Belin, Toulouse 31401, France;3. Temasek Laboratories@NTU, Nanyang Technological University, Singapore 637553, Singapore;1. School of Engineering, San Francisco State University, San Francisco, CA 94132, United States;2. Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA 22030, United States;3. Department of Electrical and Computer Engineering, Howard University, Washington, DC 20059, United States;1. Key Laboratory of Particle & Radiation Imaging, Ministry of Education, Department of Engineering Physics, Tsinghua University, Haidian District, Beijing 100084, China;2. State Key Laboratory of Intense Pulsed Irradiation Simulation and Effect, Northwest Institute of Nuclear Technology, P.O. Box 69-10, Xi''an 710024, China;3. Cogenda Co Ltd., SISPARK II Room C102-1, 1355 Jinjihu Avenue, Suzhou, Jiangsu, China |
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| Abstract: | The accurate fault prediction is of great importance in electronics high reliability applications for condition based maintenance. Traditional Particle filter (TPF) used for fault prognostic mainly uses the first-order state equation which represents the relationship between the current state and one-step-before state without considering the relation with multi-step-before states. This paper presents an optimal multi-order particle filter method to improve the prediction accuracy. The multiple τth-order state equation is established by training Least Squares Support Vector Regression (LSSVR) via electronics historical failure data, the τ value and LSSVR parameters are optimized through Genetic Algorithm (GA). The optimal τth-order state equation which can really reflect electronics degradation process is used in particle filter to predict the electronics status, remaining useful life (RUL) or other performances. An online update scheme is developed to adapt the optimal τth-order state transformation model to dynamic electronics. The performance of the proposed method is evaluated by using the testing data from CG36A transistor degradation and lithium-ion battery data. Results show that it surpasses classical prediction methods, such as LSSVR, TPF. |
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