Journal
JOURNAL OF PROCESS CONTROL
Volume 37, Issue -, Pages 46-57Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jprocont.2015.11.004
Keywords
Statistics pattern analysis; Locality preserving projections; Process monitoring; Parallel analysis; Fault detection
Funding
- National Natural Science Foundation of China [51204018]
- National Key Technology R&D Program of the 12th Five-year Plan of China [2015BAF30B01]
- Beijing Youth Talents Program of University [YETP0422]
- Fundamental Research Funds for the Central Universities [FRF-TP-15-001A3]
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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, T-2 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. (C) 2015 Elsevier Ltd. All rights reserved.
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