A new gearbox compound fault recognition approach based on improved double wavelet packet transform

A new approach for the diagnosis of compound faults in gearboxes is proposed in this paper. To extract characteristics in the frequency band of non-stationary raw vibration signals, a double-tree complex wavelet packet transform (DTCWPT) is used to decompose the signals. A singular value spectrum (SVP) is generated by performing singular value decomposition (SVD) on the matrix formed by all of the components. The new analysis method, DTCWPT-SVP, is used to diagnose different operating conditions of a gearbox, including single faults and compound faults, with a k-nearest neighbour (kNN) classifier. The results show that the minimum recognition accuracy using DTCWPT-SVP is 91.9% with different values of k in the kNN and that DTCWPT has better performance in signal decomposition than discrete wavelet packet transform (DWPT). Furthermore, the decomposition level used in DTCWPT is analysed in this paper. For the gearbox vibration problem, a level 2 or 3 DTCWPT can achieve good performance.