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An improved wavelet threshold function denoising method and its application in lifting gearbox of quayside crane
Published:2022-12-20 author:LV Sai-ge, HU Xiong, WANG Bing Browse: 1230 Check PDF documents




An improved wavelet threshold function denoising method and 
its application in lifting gearbox of quayside crane


LV Sai-ge, HU Xiong, WANG Bing

(Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China)


Abstract: The status monitoring signal (vibration signal) of the reduction gearbox of shore bridge hoisting mechanism, as one of the important loading and unloading equipment of port machinery, was disturbed by a lot of noise, and the effect of traditional denoising method was not ideal. Aiming at the problem, an improved wavelet threshold function denoising methodwas proposed. Firstly, the wavelet threshold and wavelet threshold function ware compared, selected and improved. Then, the proposed method, soft and hard threshold function denoising method, and adaptive wavelet threshold function denoising method were used to denoise the simulation signal, and the de-noising effects were compared. Finally, the same denoising methods were used to deal with the vibration signal of a quay bridge in Shanghai port, and the results of denoising were analyzed quantitatively. The results show that the signal-to-noise ratio of the signal processed by the improved denoising method is 23.0124dB, and the root mean square error is 0.2563mm/s2 in the simulation experiment. In engineering application, the improved denoising method has better effect on vibration signal denoising than the traditional method. In conclusion, the proposed improved denoising method can improve the signal-to-noise ratio of signal after denoising, reduce the root mean square error, and effectively eliminate the interference of noise to useful signals.

Key words: port machinery; shore bridge hoisting mechanism; reducer/reduction gearbox; vibration signal denoising; threshold function; signaltonoise ratio(SNR); root mean square error(RMSE)

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