Fault Detection Techniques Based on Multivariate Statistical Analysis

Abstract

Abstract:

Abstract： As an important branch of data-driven fault detection methods, multivariate statistical analysis-based fault detection methods mainly include principal component analysis, partial least squares, independent component analysis and fisher discriminant analysis. In this paper, the data model and fault detection mechanism of each method mentioned above were reviewed. Several properties of these methods were revealed intuitively using simulation results, and their fault detection abilities were illustrated. Finally, several problems related to data-driven fault detection methods were discussed.

Key words: multivariate statistical analysis, principal component analysis, partial least squares, independent component analysis, Fisher discriminant analysis

CLC Number:

TP 273

JI Hongquan,HE Xiao,ZHOU Donghua. Fault Detection Techniques Based on Multivariate Statistical Analysis[J]. Journal of Shanghai Jiaotong University, 2015, 49(06): 842-848.

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