Fault diagnosis of low-speed rolling bearing based on quality-adjustable wavelet denoising

In view of the problem that low-speed running rolling bearing fault characteristics are prone to noise annihilation, a hierarchical adaptive threshold denoising method based on (TQWT) is proposed, and the method is combined with envelope spectrum analysis for fault analysis and diagnosis of low-speed bearings. First, the collected bearing vibration signal is TQWT decomposed to obtain the decomposed wavelet coefficient; then construct the hierarchical adaptive threshold function using Sigmoid function to threshold the high frequency coefficient of TQWT; finally, combined with the high frequency wavelet coefficient and low frequency wavelet coefficient to reconstruct the signal to obtain the denoising bearing vibration signal. Through the experimental platform collected in low speed bearing fault vibration signal experimental analysis, experimental results show that the method has achieved good denoising effect, in reducing noise interference at the same time effectively retain the bearing fault feature information, denoising the signal envelope spectrum can clearly present fault spectrum characteristics, can observe the fault characteristics of multiple frequency peak, and peak interference near the few, effectively improve the low speed running bearing fault diagnosis rate;