Feature Extraction for Bearing Fault Detection Using Wavelet Packet Energy and Fast Kurtogram Analysis

Xiaojun Zhang,Yaqi Wu,Dong Zhen,Jirui Zhu,Minglu Zhang

doi:10.3390/app10217715

Abstract

An integrated method for fault detection of bearing using wavelet packet energy (WPE) and fast kurtogram (FK) is proposed. The method consists of three stages. Firstly, several commonly used wavelet functions were compared to select the appropriate wavelet function for the application of WPE. Then the analyzed signal is decomposed using WPE and the energy of each decomposed signal is calculated and selected for signal reconstruction. Secondly, the reconstructed signal is analyzed by FK to select the best central frequency and bandwidth for the band-pass filter. Finally, the filtered signal is processed using the squared envelope frequency spectrum and compared with the theoretical fault characteristic frequency for fault feature extraction. The procedure and performance of the proposed approach are illustrated and estimated by the simulation analysis, proving that the proposed method can effectively extract the weak transients. Moreover, the analysis results of gearbox bearing and rolling bearing cases show that the proposed method can provide more accurate fault features compared with the individual FK method.

Highlights

The condition monitoring of rolling bearings is very important in ensuring the safety of the mechanical system as it is one of the basic components and widely applied in various rotating machines
To effectively extract fault features from background noise, this paper proposes a hybrid method based on wavelet packet energy (WPE) and fast kurtogram (FK) for bearing fault diagnosis
This paper proposes a new method using wavelet packet energy (WPE) and fast kurtogram (FK)

Summary

Introduction

The condition monitoring of rolling bearings is very important in ensuring the safety of the mechanical system as it is one of the basic components and widely applied in various rotating machines. The measured dynamic signal of bearing has the characteristics of non-linear and non-stationary with various background noise. The original signal is too low in energy to extract fault characteristics. The vibration signal produced by the rotating machine contains significant information related to the state of the machine; the vibration-based analysis has been widely used as an effective method to identify the machine faults [4]. The measured vibration signal is consisted of stationary, non-stationary, and background noise. It is necessary to select appropriate signal processing technology to obtain effective fault features for rolling bearings fault detection [5,6,7]

Methods

Results

Conclusion