Optimized Analysis of Mechanical Vibration Signals Through Improved Wavelet Threshold Denoising

Wavelet transform is highly regarded as a mathematical theory and method in the fields of signal analysis and image processing. Vibration signals are crucial in mechanical fault diagnosis, but the collected vibration signals usually contain noise, so the vibration signals need to be denoised first. In wavelet denoising, the hard threshold function easily triggers oscillations, while the soft threshold function has the problem of constant deviation. This paper proposes the enhanced threshold function for vibration signal denoising on the basis of previous research, and evaluates the denoising effect with the signal-to-noise ratio and the mean-square error as the indexes, and the results show that choosing the appropriate wavelet basis function and decomposition level, combining with the adaptive threshold rule and the enhanced threshold function, the signal-to-noise ratio can be effectively improved and the mean-square error can be reduced. This study provides a practical solution for signal processing in the field of mechanical fault diagnosis.