Ball-Bearing Fault Classification Using Comparative Analysis of Wavelet Coefficient based on Entropy Measurement

Abstract

Ball bearing is used to provide free rotation around a fixed axis. Various kinds of faults exist in the bearing such as inner race fault, outer race fault and ball race fault. One very effective method to diagnose the bearing fault is vibration signal analysis. Empirical mode decomposition (EMD) has been used for ball-bearing fault diagnosis in mechanical systems using vibration signal analysis. Classification of the ball-bearing fault has always been a challenging task. Various classification schemes such as Extreme learning machine (ELM), K-means Clustering, and Support vector machine (SVM), have been reported in the literature for ball-bearing fault classification. SVM is restricted by multiclass classification efficiency, and ELM is restricted by the longer training. In this paper, the entropy analysis of the wavelet coefficient obtained from the third level decomposition of the residue signal (obtained after subtracting the highest frequency component from the raw signal) has been done for ball-bearing fault classification. A comparative analysis of the wavelet coefficient based on entropy measurement has been presented here. High fault classification accuracy has been achieved using the proposed method for the detection of ball-bearing fault. Shannon entropy, Average Shannon entropy, and Renyi’s entropy are parameters for the justification of the proposed approach. The result shows the best wavelet to be chosen among the available discrete wavelet based on various entropy measurements.

Keywords:

•  Average Shannon entropy
• Ball bearing
• EMD
• IMF
• Maximum energy to Shannon entropy
• Renyi’s entropy
• Shannon Entropy
• Wavelet

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Rahul Dubey

Rahul Dubey is an assistant professor in the Department of Electronics Engineering, Madhav Institute of Technology and Science, Gwalior, India. His research interests are signal processing and machine learning. Email: [email protected]

Vikram Rajpoot

Vikram Rajpoot is an assistant professor in the Department of Information Technology, Madhav Institute of Technology and Science, Gwalior, India. His research interests are image processing and machine learning. Email: [email protected]

Ankur Chaturvedi

Ankur Chaturvedi is an assistant professor in the Department of Computer Science Engineering and Application, GLA University, Mathura India. His research interests are image processing and machine learning.

Abhishek Dixit

Abhishek Dixit is an assistant professor in the Department of Information Technology, Madhav Institute of Technology and Science, Gwalior, India. His research interest are image processing and machine learning. Email: [email protected]

Saumil Maheshwari

Saumil Maheshwari is an assistant professor in the Department of Information Technology, Madhav Institute of Technology and Science, Gwalior, India. His research interests are image processing and deep learning. Email: [email protected]