MCSKPCA based neural network fault diagnosis method for analog circuits

• Main Authors: XIAO YINGQUN, YANG HUI, FANG GEFENG, HE YIGANG
• Format: Patent
• Language: Chinese; English
• Published: 14.12.2011
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; MEASURING; MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES; PHYSICS; TESTING

Disclosed is an MCSKPCA based neural network fault diagnosis method for analog circuits, comprising acquiring the output voltage signal of an analog circuit to be diagnosed; performing wavelet transformation on the acquired output voltage signal; calculating the energy eigenvalues of the wavelet coefficients of the output voltage signal, obtained through the wavelet transformation; performing MCSKPCA feature extraction and dimensionality reduction on the energy eigenvalues, and obtaining an optical eigenvector; and sending the optical eigenvector to a BP neural network separator, and outputting a fault diagnosis result by the BP neural network separator. The method can be used for not only diagnosis of linear or nonlinear circuits and systems thereof, but also diagnosis of hard fault and soft fault in the linear or nonlinear circuits.