Rolling bearing fault diagnosis based on adaptive smooth ITD and MF-DFA method

Zhe Yuan,Daniel Cristea,Dong An,Tingting Peng,Mihai Alin Pop

doi:10.1177/1461348419867012

Abstract

To effectively utilize a feature set to further improve fault diagnosis of a rolling bearing vibration signal, a method based on multi-fractal detrended fluctuation analysis (MF-DFA) and smooth intrinsic time-scale decomposition (SITD) was proposed. The vibration signal was decomposed into several proper rotation components by applying this new SITD method to overcome noise effects, preserve the effective signal, and improve the signal-to-noise ratio. Wavelet analysis was embedded in iteration procedures of intrinsic time-scale decomposition (ITD). For better results, an adaptive threshold function was used for signal recovery from noisy proper rotation components in the wavelet domain. Additionally, MF-DFA was used to reveal the multi-fractality present in the instantaneous amplitude of the proper rotation components. Finally, linear local tangent space alignment was applied for feature dimension reduction and to obtain fault characteristics of different types, further improving identification accuracy. The performance of the proposed method is determined to be superior to that of the ITD-MF-DFA method.

Highlights

Because of complex operating environmental and objective factors, rolling bearing failures frequently occur in industrial processes
With the purpose of effectively using the sensitive features contained in feature set for fault diagnosis and overcoming the noise interference problem, a new SITD decomposition method based on intrinsic time-scale decomposition (ITD) was proposed
To improve the accuracy of feature extraction in fault diagnosis, a new fault diagnosis approach involving rolling bearing decomposition based on SITD and multi-fractal detrended fluctuation analysis (MF-DFA) was proposed

Summary

Introduction

Because of complex operating environmental and objective factors, rolling bearing failures frequently occur in industrial processes. Researchers have achieved much progress in fault detection and diagnosis methods based on WT.[9,10,11,12] With the purpose of improving the adaptability of wavelet applications, an adaptive threshold function is presented in this study, which was used to improve the efficiency of the denoising procedure. With the purpose of effectively using the sensitive features contained in feature set for fault diagnosis and overcoming the noise interference problem, a new SITD decomposition method based on ITD was proposed. For the purpose of eliminating the noise signal, the SITD method was proposed, in which wavelet analysis was embedded in the iteration procedures of ITD, which could preserve the effective signal and improve the SNR. To more effectively preserve the signal singularity in the signal denoising process, the threshold function g(x, th, m) applicable to wavelet coefficients at different scales could be selected by adjusting the parameter m in equation (14). The operation procedures of SITD are as follows: 1. Compute the PRCs based on the procedure discussed previously

Calculate the wavelet coefficients by the following equation

Conclusions