期刊
CANADIAN JOURNAL OF CHEMICAL ENGINEERING
卷 96, 期 5, 页码 1116-1126出版社
WILEY
DOI: 10.1002/cjce.23058
关键词
nonlinear process; fault detection; modified KPLS; statistical local approach
资金
- National Natural Science Foundation of China [61403256, 61374132]
Conventional kernel partial least squares (KPLS) may not function well for detecting incipient faults in nonlinear processes. In relation to existing work, a new statistical local approach based KPLS monitoring strategy is proposed by integrating the statistical local approach into a modified KPLS framework. The advantages of the proposed technique are that (i) the new score variables constructed in the statistical local approach approximately follow Gaussian distribution, in spite of the distribution that the original data follow; (ii) after whitening of the data using KPCA, the dimension of modelling space is greatly reduced, which will definitely improve the computing speed. The new method shows more effective and sensitive performance for detecting incipient faults or slow changes of processes. This is demonstrated by a simulation numerical example and recorded data from a non-isothermal CSTR process.
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