Wavelet analysis with time-synchronous averaging of planetary gearbox vibration data for fault detection and diagnostics

Vibration data analysis plays a critical part in condition monitoring for fault detection and diagnostics. In this paper, an approach to planetary gearbox fault detection based on the application of wavelet transform to the time synchronously averaged (TSA) signal is presented. The autocovariance of maximal energy coefficients based on wavelet transform is proposed to evaluate planetary gearbox fault advancement quantitatively. For a comparison, the advantages and disadvantages of some approaches such as using variance, kurtosis, RMS and crest factor for original signal, TSA, continuous wavelet transform (CWT) and discrete wavelet transform (DWT) applied to TSA signal, as well as CWT and DWT applied to the TSA signal with autocovariance, are discussed. It has been demonstrated that the method based on wavelet transform combined with the approaches mentioned above can achieve desirable feature extraction.