An approach of fault detection based on multi-mode

Conventional multi-scale principal component analysis (MSPCA) only detects fault, but it canpsilat detect fault types. For these problems, a method of fault detection based on multi-mode that incorporates MSPCA into adaptive resonance (ART) neural network is presented. Firstly, this method presents a wavelet transform for samples data, and principal component analysis can be used to analyze data at each scale. Then ART is used to classify reconstruction data. It can detect fault effectively, and ART2 can classify fault using wavelet denoising easily, it separates the fault successfully in the system. At last, it develops multi-mode fault detection in autocorrelation system application through computer simulation experiment. The theory and simulation experiments shows that this method is of wide application prospect.