Statistics local fisher discriminant analysis for industrial process fault classification

Deng, Xiaogang, Tian, Xuemin, Chen, Sheng and Harris, Christopher (2016) Statistics local fisher discriminant analysis for industrial process fault classification. In Control (CONTROL), 2016 UKACC 11th International Conference on. IEEE. 6 pp . (doi:10.1109/CONTROL.2016.7737588).

Record type: Conference or Workshop Item (Paper)

Abstract

In order to effectively identify industrial process faults, an improved Fisher discriminant analysis (FDA) method, referred to as the statistics local Fisher discriminant analysis (SLFDA), is proposed for fault classification. For mining statistics information hidden in process data, statistics pattern analysis is firstly applied to transform the original measured variables into the corresponding statistics, including second-order and higher-order ones. Furthermore, considering the local structure characteristics of fault data, local FDA (LFDA) is performed which computes the discriminant vectors by modifying the optimization objective with local weighting factor. Simulation results on the benchmark Tennessee Eastman process show that the proposed SLFDA has a better fault classification performance than the FDA and LFDA methods.

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