Deep Transfer Learning Framework for Bearing Fault Detection in Motors

Prashant Kumar, Prince Kumar, Ananda Shankar Hati and Heung Soo Kim ()
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Prashant Kumar: Department of Mechanical, Robotics, and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 gil, Jung-gu, Seoul 04620, Republic of Korea
Prince Kumar: Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, Jharkhand, India
Ananda Shankar Hati: Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, Jharkhand, India
Heung Soo Kim: Department of Mechanical, Robotics, and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 gil, Jung-gu, Seoul 04620, Republic of Korea

Mathematics, 2022, vol. 10, issue 24, 1-14

Abstract: The domain of fault detection has seen tremendous growth in recent years. Because of the growing demand for uninterrupted operations in different sectors, prognostics and health management (PHM) is a key enabling technology to achieve this target. Bearings are an essential component of a motor. The PHM of bearing is crucial for uninterrupted operation. Conventional artificial intelligence techniques require feature extraction and selection for fault detection. This process often restricts the performance of such approaches. Deep learning enables autonomous feature extraction and selection. Given the advantages of deep learning, this article presents a transfer learning–based method for bearing fault detection. The pretrained ResNetV2 model is used as a base model to develop an effective fault detection strategy for bearing faults. The different bearing faults, including the outer race fault, inner race fault, and ball defect, are included in developing an effective fault detection model. The necessity for manual feature extraction and selection has been reduced by the proposed method. Additionally, a straightforward 1D to 2D data conversion has been suggested, altogether eliminating the requirement for manual feature extraction and selection. Different performance metrics are estimated to confirm the efficacy of the proposed strategy, and the results show that the proposed technique effectively detected bearing faults.

Keywords: deep learning; transfer learning; prognostics and health management; bearing fault; electrical motor (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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