Fault detection strategy combining NARMAX model and Bhattacharyya distance for process monitoring |
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| Authors: | Lakhdar Aggoun Yahya Chetouani |
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| Institution: | 1. Automatic Laboratory of Setif, Department of Electrical Engineering, Setif 1 University, Maabouda, Street of Bejaia, Setif 19000, Algeria;2. Université de Rouen, IUT, Chemical Engineering Department, Rue Lavoisier, Mont-Saint-Aignan 76130, France;1. School of Computer and Information Engineering, Henan University, Kaifeng 475004, China;2. Miami College of Henan University, Henan University, Kaifeng 475004, China;3. Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China;1. Department of Control and Automation Engineering, National Korea Maritime and Ocean University, Busan 49112, South Korea;2. School of IT Information and Control Engineering, Kunsan National University, Kunsan 54150, South Korea;1. School of Electronics Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Republic of Korea;2. School of Architecture and Civil Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Republic of Korea;1. School of Engineering, Huzhou University, Huzhou 313000, Zhejiang, China;2. Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;3. College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China;1. Departamento de Control Automatico, CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico;2. Departamento de Computacion, CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico;1. School of Information Engineering, Fuyang Normal University, Fuyang 236041, PR China;2. College of Mathematics and Statistics, Guangxi Normal University, Guilin 541006, PR China;3. School of Information Science and Engineering, Chengdu University, Chengdu 610106, PR China;4. School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, PR China;5. School of Mathematics, Southeast University, Nanjing 211189, China;6. Yonsei Frontier Lab, Yonser University, Seoul 03722, Korea |
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| Abstract: | The complexity of modern chemical and petrochemical plants is becoming increasingly problematic in the recent years. At the same time, the demands to ensure safety and reliability of process operations rise. Early detection of abnormal event in complex real systems decrease maintenance cost and lead to guarantee the safety of human operators and environment. In the present work, a fault detection (FD) method which exploits the advantages of black-box modeling and statistical measure for fault detection in real chemical process as a distillation column is proposed. This technique is developed by applying the Nonlinear Auto-Regressive Moving Average with eXogenous input (NARMAX) model and Bhattacharyya distance (BD). In order to determine the NARMAX model, a real data set recorded during normal operations is used. Then, the BD is used to quantify on-line the dissimilarity between the current and reference probability distributions of the residual obtained from the NARMAX model for fault detection purposes. The ability of the proposed FD approach is demonstrated using real fault of separation unit. The obtained results indicate that the developed technique produces favorable performance compared to the conventional Cumulative Sum (CUSUM) test. |
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