SCRS Conference Proceedings on Intelligent Systems
Bearing various defects classification using deep network learning method
Authors: B. Belkacemi and S. Saad
Publishing Date: 25-04-2022
ISBN: 978-93-91842-08-6
Abstract
Diagnostics and classification of faults are critical today, especially in rotating machines, to prevent material and human losses. In order to classify bearing faults, this paper utilizes artificial intelligence methods, namely deep network learning. This field has used a variety of methods to detect and predict bearing faults, but none of them are foolproof or perfect. There are many drawbacks to most of these methods, such as the difficulty of extracting vibration signals. As a result of using deep network learning in the present study, the accuracy of the results has been improved compared to the previous methods. The results have showed that the proposed technique is superior to the previously studied method and can be used to classify induction motor bearing faults effectively.
Keywords
Rotating machines, Fault diagnosis, Bearing defects, Deep network learning, Faults classification.
Cite as
B. Belkacemi and S. Saad, "Bearing various defects classification using deep network learning method", In: Raju Pal and Praveen Kumar Shukla (eds), SCRS Conference Proceedings on Intelligent Systems, SCRS, India, 2022, pp. 371-379. https://doi.org/10.52458/978-93-91842-08-6-37