Check valve's fault detection with wavelet packet's kernel principal component analysis

Abstract

High pressure piston diaphragm pump is the most important power source of the pipeline transportation.To solve the problem of on-line monitoring on the fault of internal piston,the authors put forward a detection method based on acoustic emission signal's wavelet packet frequency and Kernel Principal Component Analysis(KPCA).Firstly,the author adopted wavelet packet to deal with the acoustic emission data to get each frequency band energy value.Secondly,the authors used KPCA to decompose the energy in high dimensional space to find the feature model,and made use of statistics SPE and T2 in feature model to make detection on fault signal.Finally,the authors conducted experiments to verify the statistics of acoustic emission of GEHO diaphragm pump's check valve.In comparison with the PCA method,the proposed method can make on-line monitoring on fault of internal piston fast and accurate,so it has good application prospect on the domain of the high pressure piston diaphragm pump's non-destructive fault detection.