Bispectrum entropy feature extraction and its application for fault diagnosis of gearbox

Abstract

Fault feature extraction and application is the key technology of gearbox fault diagnosis. In this paper, a fault diagnosis method using bispectrum entropy as the fault feature parameters is put forward. Bispectrum entropy as the information entropy in bispectrum domain can reflect the complexity of information energy. When the structure is failed, the distribution of bispectrum will be changed. bispectrum entropy can reflect this change and achieve good separation of the different types of fault. In this paper, the vibration signal in different states of a secondary drive gearbox is compared and analyzed, bispectrum and bispectrum entropy are extracted. Feature vector is set up via bispectrum entropy for the fault pattern recognition and diagnosis by BP neural network. The analysis result proves that bispectrum entropy is more sensitive to fault characteristic and can separate the fault of gearbox. Via applying this method, the numerical characteristics extraction and intelligent diagnosis will be ease realized easily.