A Novel Fault Diagnosis Approach for Rolling Bearing Based on High-Order Synchrosqueezing Transform and Detrended Fluctuation Analysis

Wei Liu,Wei Chen,Zhihua Zhang

doi:10.1109/access.2020.2965744

Abstract

Time-frequency analysis always plays an important role in machine health monitoring owing to its advantage in extracting the fault information contained in non-stationary signal. In this paper, we present a novel technique to detect and diagnose the rolling bearing faults based on high-order synchrosqueezing transform (FSSTH) and detrended fluctuation analysis (DFA). With this method, the high-order synchrosqueezing transform is first utilized to decompose the vibration signal into an ensemble of oscillatory components termed as intrinsic mode functions (IMFs). Meanwhile, an empirical equation, which is based on the DFA, is introduced to adaptively determine the number of IMFs from FSSTH. Then, a time-frequency representation originated from the decomposed modes or corresponding envelopes is exhibited in order to identify the fault characteristic frequencies related to rolling bearing. Experiments are carried out using both simulated signal and real ones from Case Western Reserve University. Results show that the proposed method is more effective for the detection of fault characteristic frequencies compared with the traditional synchrosqueezing transform (SST) based fault diagnosis algorithm, which renders this technique is promising for machine fault diagnosis.

Highlights

Rolling bearing is widely used in various industrial machines and is considered one of the most stressed parts in rotating machinery
We propose a metric based on detrended fluctuation analysis (DFA) to determine the number of intrinsic mode functions (IMFs) from FSSTH, and present a new method for the fault detection of rolling bearing, which makes full use of the advantage of FSSTH in extracting instantaneous frequencies with higher precision
Non-stationary signal analysis is an important issue for machine fault diagnosis, especially when the machine is running under complex working conditions

Summary

INTRODUCTION

Rolling bearing is widely used in various industrial machines and is considered one of the most stressed parts in rotating machinery. In 2017, Pham and Meignen [31] proposed a new adaptive signal analysis algorithm that is known as high-order synchrosqueezing transform (FSSTH) It is a new generalization of the STFT-based synchrosqueezing transform by computing more accurate estimates of the instantaneous frequencies using higher order approximations both for the amplitude and phase, which results in perfect concentration and reconstruction for a wider variety of signals. The high-order synchrosqueezing transform is a new extension of the conventional STFT-based SST (FSST) that was firstly proposed by Thakur and Wu [30], which achieves more accurate estimates of the instantaneous frequencies by using higher order approximations both for the amplitude and phase [31]. Where d denotes the compensation factor and φ (t) is an estimate for φ (t)

DETRENDED FLUCTUATION ANALYSIS

SIMULATION TEST

CONCLUSION