Synchrosqueezing-cross Wavelet Transform and Enhanced Fault Diagnosis of Rolling Bearing
LI Ji-meng,HUANG Meng-jun,XIE Ping,JIANG Guo-qian,CHEN Meng,HE Qun
Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
Abstract：In order to more effectively monitor and diagnose bearing faults, an enhanced fault diagnosis method was proposed based on synchrosqueezing-cross wavelet transform(SXWT). First, this method segmented the signal into two sub-signals with the same length. Then, using the synchrosqueezing wavelet transform(SWT) deals with the two sub-signals respectively, so as to obtain synchrosqueezing wavelet transform coefficients to be used as the input of cross wavelet transform(XWT). Finally, the obtained cross wavelet scale spectrum was used to extract the characteristic frequency information, thus achieving the enhanced fault diagnosis of bearings. The method is applied to the bearing fault diagnosis. Compared with continuous wavelet transform(CWT), XWT and SWT, the proposed method can effectively extract the detail characteristics of bearing signal in the time-frequency domain to enhance the readability of the bearing characteristics frequency in the time-frequency domain, and realize accurate and reliable diagnosis of the bearings faults.
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2018, Vol. 39 
