Journal
CONTROL ENGINEERING PRACTICE
Volume 43, Issue -, Pages 39-48Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.conengprac.2015.05.010
Keywords
Kullback-Leibler divergence; Auto-and cross correlation; Subspace identification; Non-Gaussian distribution; Incipient fault conditions
Funding
- National Natural Science Foundation of China [61203088, 61134007, 61374121]
- 111 Project from Ministry of Education, China [B07031]
- Petroleum Institute [RIFP 14301, RIFP 15332]
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This paper proposes detecting incipient fault conditions in complex dynamic systems using the Kullback-Leibler or KL divergence. Subspace identification is used to identify dynamic models and the KL divergence examines changes in probability density functions between a reference set and online data. Gaussian distributed process variables produce a simple form of the KL divergence. Non-Gaussian distributed process variables require the use of a density-ratio estimation to compute the KL divergence. Applications to recorded data from a gearbox and two distillation processes confirm the increased sensitivity of the proposed approach to detect incipient faults compared to the dynamic monitoring approach based on principal component analysis and the statistical local approach. (C) 2015 Elsevier Ltd. All rights reserved.
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