Fault Detection Techniques Based on Multivariate Statistical Analysis
JI Hongquan,HE Xiao,ZHOU Donghua
(Department of Automation, TNList, Tsinghua University, Beijing 100084, China)
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.
Received: 15 January 2015
Published: 29 June 2015
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