国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

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，Tunable Q-factor Wavelet Transform) 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. Experimental results show that compared with the classical soft threshold function and the hard threshold function, the method in this paper can obtain better denoising effect.While reducing noise interference, the fault characteristic information of the bearing is effectively preserved.The envelope spectrum of the signal after denoising can clearly show the spectral characteristics of the fault, and the multi-frequency peak of the fault characteristic can be observed, and there is little interference near the peak.Which effectively improves the diagnosis accuracy of early bearing faults.In the simulation signal experiment, compared with the soft threshold and hard threshold functions, the method in this paper has higher signal-to-noise ratio (SNR) and lower root mean square error (RMSE) after denoising.Compared with the hard threshold function,the SNR of the proposed method increased by 4.1491 on average, and the RMSE decreased by an average of 0.1329; compared with the soft threshold function, the average SNR of the proposed method increased by 5.1118, and the average RMSE decreased by 0.1505.