A sequential probability ratio test (SPRT) to detect changes and process safety monitoring |
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| Institution: | 1. National Fuel Cell Vehicle & Powertrain System Research & Engineering Center, No. 4800 Caoan Road, Shanghai 201804, China;2. School of Automotive Studies, Tongji University, No. 4800 Caoan Road, Shanghai 201804, China;1. School of Electronic & Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China;2. Energy Department, Politecnico di Milano, Via La Masa 34/3, 20156, Italy;3. MINES ParisTech, PSL Research University, CRC, Sophia Antipolis, France;4. Engineering School, DEIM, University of Tuscia, 01100 Viterbo, Italy;5. Ocean College, Zhejiang University, Zhejiang 316021, PR China;1. Research & Development Center, Korea Testing Certification, Gunpo, Republic of Korea;2. Department of Industrial Engineering, Graduate School of Ajou University, Suwon, Republic of Korea;3. Reliability Physics Research Center, Korea Electronic Technology, Seongnam, Republic of Korea;1. Politecnico di Milano, Dipartimento di Meccanica, Milan, Italy;2. University of California San Diego, Department of Structural Engineering, San Diego, CA, United States |
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| Abstract: | Detecting anomalies is an important problem that has been widely researched within diverse research areas and application domains. The early detection of faults may help avoid product deterioration, major damage to the machinery itself and damage to human health. This study proposes a robust fault detection method with an Artificial Neural Network-Multi-Layer Perceptron (ANN-MLP) and a statistical module based on Wald's sequential probability ratio test (SPRT). To detect a fault, this method uses the mean and the standard deviation of the residual noise obtained from applying a NARX (Nonlinear Auto-Regressive with eXogenous input) model. To develop the neural network model, the required training and testing data were generated at different operating conditions. To show the effectiveness of the proposed fault detection method, it was tested on a realistic fault of a distillation plant at the laboratory scale. |
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