Adaptive Extraction Method Based on Time-Frequency Images for Fault Diagnosis in Rolling Bearings of Motor

Yunchao Ma,Cheng Wang,Chengdong Wang,Dongchen Yang,Fazal Mahomed

doi:10.1155/2021/6687195

Yunchao Ma, Cheng Wang + Show 3 more

Open Access

https://doi.org/10.1155/2021/6687195

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Abstract

In order to diagnose the faults of rolling bearings in motors via time-frequency analysis of bearing vibration signals quickly, this paper puts forward a method of extracting the main components from time-frequency images. A threshold is adaptively determined based on the gray histogram feature of the time-frequency images obtained from the vibration signals of the motor rolling bearings. Then, a mask template is generated by the threshold and a binarization processing. Based on a multiplication operation between the mask template and the original time-frequency image, the signal component with low energy in the time-frequency image is filtered out, and only the main components with high energy is remained for fault diagnosis, which is convenient for the subsequent identification of the faults for motor rolling bearings. The main components in the time-frequency images can be retained adaptively with the thresholds determined by the time-frequency images themselves.

Highlights

Condition monitoring and fault diagnosis for equipment can monitor the health status of equipment in real time and determine the fault location and severity by the changes of some signals, which can avoid the occurrence of major accidents and greatly save maintenance costs
While a motor is working, factors such as overload impact, assembly error, poor lubrication, or impurity doping will lead to the failure of the bearing. e vibration signals of a motor will show the unsteady characteristic, and the nonstationary signals have the characteristics of limited duration and timely variation. e traditional signal processing methods are mostly based on the assumption of a stable state, which can only analyze the statistical characteristics of the signal in the time domain or frequency domain, but are unable to reveal the instantaneous characteristics in the joint time-frequency domain. e timefrequency representation of a signal can describe the energy distribution and time-varying characteristics in the timefrequency domain, which is the most complete expression method for unstable signals
Cai et al [5] proposed a new fault diagnosis method based on the time-frequency image recognition of EMD-Wigner–Ville distributions (WVD) vibration spectrums by SVM

Summary

Introduction

Condition monitoring and fault diagnosis for equipment can monitor the health status of equipment in real time and determine the fault location and severity by the changes of some signals, which can avoid the occurrence of major accidents and greatly save maintenance costs. 2. The Method of Extracting the Main Components from Time-Frequency Images At is to say, the features of faults are largely contained in the main components whose energy is expressed with large gray values in the time-frequency image.

Results

Conclusion