A novel process monitoring and fault detection approach based on statistics locality preserving projections |
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| Affiliation: | 1. School of Information Science and Technology, Yunnan Normal University, Kunming 650092, China;2. The Engineering Research Center of GIS Technology in Western China of Ministry of Education, 650500, China;3. The Laboratory of Pattern Recognition and Artificial Intelligence, Yunnan Normal University, Kunming, Yunnan 650092, China;1. School of Mechanical Engineering and Automation, Fuzhou University, Minhou Country Xueyuan Road No. 2, 350106 Fuzhou, China;2. Fujian Metrology Institute, Pingdong Road No. 9, 350003 Fuzhou, China;3. The Military Engineering Innovation Center for Knitted Wire Mesh of Fujian Province, Fuzhou University, Minhou Country Xueyuan Road No. 2, 350106 Fuzhou, China;1. Institute of Data Science and Statistics, Shanghai University of Finance and Economics, Shanghai 200433, China;2. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China;3. College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China;4. School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China |
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| Abstract: | Data-driven fault detection technique has exhibited its wide applications in industrial process monitoring. However, how to extract the local and non-Gaussian features effectively is still an open problem. In this paper, statistics locality preserving projections (SLPP) is proposed to extract the local and non-Gaussian features. Firstly, statistics pattern analysis (SPA) is applied to construct process statistics and grasp the non-Gaussian statistical property using high order statistics. Then, locality preserving projections (LPP) method is used to discover local manifold structure of the statistics. In essence, LPP tries to map the close points in the original space to close in the low-dimensional space. Lastly, T2 and squared prediction error (SPE) charts of SLPP model are used to detect process faults. One simple simulated system and the Tennessee Eastman process show that the proposed SLPP method is more effective than principal component analysis, LPP and statistics principal component analysis in fault detection performance. |
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| Keywords: | Statistics pattern analysis Locality preserving projections Process monitoring Parallel analysis Fault detection |
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