Gear fault diagnosis based on a new wavelet adaptive threshold de-noising method

Abstract

Purpose

This paper aims to explore a new wavelet adaptive threshold de-noising method to resolve the shortcomings of wavelet hard-threshold method and wavelet soft-threshold method, which are usually used in gear fault diagnosis.

Design/methodology/approach

A new threshold function and a new determined method of threshold for each layer are proposed. The principle and the implementation of the algorithm are given. The simulated signal and the measured gear fault signal are analyzed, and the obtained results are compared with those from wavelet soft-threshold method, wavelet hard-threshold method and wavelet modulus maximum method.

Findings

The presented wavelet adaptive threshold method overcomes the defects of the traditional wavelet threshold method, and it can effectively eliminate the noise hidden in the gear fault signal at different decomposition scales. It provides more accurate information for the further fault diagnosis.

Originality/value

A new threshold function is adopted and the multi-resolution unbiased risk estimation is used to determine the adaptive threshold, which overcomes the defect of the traditional wavelet method.

Citation

Cai, J. (2019), "Gear fault diagnosis based on a new wavelet adaptive threshold de-noising method", Industrial Lubrication and Tribology, Vol. 71 No. 1, pp. 40-47. https://doi.org/10.1108/ILT-03-2018-0101