Automated bearing fault classification based on discrete wavelet transform method
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ORIGINAL RESEARCH
Automated bearing fault classification based on discrete wavelet transform method R. Shukla1 · P. K. Kankar2 · R. B. Pachori3 Received: 5 March 2020 / Accepted: 9 September 2020 © Society for Reliability and Safety (SRESA) 2020
Abstract The prediction of the current state of the rotatory mechanical components like bearing needs to be of utmost accuracy for the efficient operation of the machine. As the bearing is normally employed at the supporting ends of the power transmission line, manual monitoring by the disengagement of the bearing is not a feasible option. Thus, there is a need for highly accurate machine learning algorithms that can successfully detect the abnormalities by capturing the running characteristics of the system. One such characteristic can be a vibration signal. In this article, various autonomous methods for condition monitoring are compared by employing machine learning and ensemble learning methods for fault classification in the bearings. Three different types of faults namely inner race fault, outer race fault, and ball fault have been considered. Discrete wavelet transform is applied to the raw signals obtained for healthy and these faulty conditions. The feature vector is extracted by decomposition and reconstruction of signals for ten different levels. The extracted feature vector is fed to three different classifiers: XGBoost, decision tree, and support vector machine. The results reveal that XGBoost outperforms the other two classifiers. The proposed manuscript signifies the capability of XGBoost classifier even if treated with readily fewer instances of raw signals. Keywords Bearing fault classification · Ensemble learning · XGBoost · Discrete wavelet transform · Machine learning
1 Introduction The wide range of applications of rotary mechanical equipment in industries leads to the efficient working of the system. Thus, it is the need of the hour to make it suitable to deliver a large amount of power with very little human efforts. Mechanical equipment has many subcomponents that play a vital role in the proper functioning of the whole * P. K. Kankar [email protected] R. Shukla [email protected] R. B. Pachori [email protected] 1
Department of Electronics Engineering, Bharati Vidyapeeth (Deemed to be) University, College of Engineering Pune, Pune 411043, India
2
Discipline of Mechanical Engineering, Indian Institute of Technology Indore, Indore 453552, India
3
Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India
system and failure of these components sometimes may cost a lot of problems and loss. Rolling components like bearing is one of such important subcomponents. As demand for high-speed machines is increasing, chances of failure also get higher for such components, thus, it can be treated as one of the critical elements in the system. Thus, the proper maintenance of such components in healthy conditions for the most productive output of the system is highly required. Fault diagnosis is one o
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