Gearbox fault diagnosis using improved feature representation and multitask learning

Sohaib, M., Munir, S., Islam, M. M. M., Shin, J., Tariq, F. , Ar Rashid, S. M. M. and Kim, J.-M. (2022) Gearbox fault diagnosis using improved feature representation and multitask learning. Frontiers in Energy Research, 10, 998760. (doi: 10.3389/fenrg.2022.998760)

Abstract

A gearbox is a critical rotating component that is used to transmit torque from one shaft to another. This paper presents a data-driven gearbox fault diagnosis system in which the issue of variable working conditions namely uneven speed and the load of the machinery is addressed. Moreover, a mechanism is suggested that how an improved feature extraction process and data from multiple tasks can contribute to the overall performance of a fault diagnosis model. The variable working conditions make a gearbox fault diagnosis a challenging task. The performance of the existing algorithms in the literature deteriorates under variable working conditions. In this paper, a refined feature extraction technique and multitask learning are adopted to address this variability issue. The feature extraction step helps to explore unique fault signatures which are helpful to perform gearbox fault diagnosis under uneven speed and load conditions. Later, these extracted features are provided to a convolutional neural network (CNN) based multitask learning (MTL) network to identify the faults in the provided gearbox dataset. A comparison of the experimental results of the proposed model with that of several already published state-of-the-art diagnostic techniques suggests the superiority of the proposed model under uneven speed and load conditions. Therefore, based on the results the proposed approach can be used for gearbox fault diagnosis under uneven speed and load conditions.

• Item Type: Articles
• Additional Information: Funding: This research was financially supported by the Ministry of Small and Medium-sized Enterprises (SMEs) and Startups (MSS), Korea, under the “Regional Specialized Industry Development Plus Program (R & D, S3092711)” supervised by the Korea Institute for Advancement of Technology(KIAT). This work was also supported by the Korea Technology and Information Promotion Agency (TIPA) grant funded by the Korea government(SMEs) (No. S3126818).
• Keywords: Energy Research, gearbox, fault diagnosis and prognosis, condition-based monitoring, feature learning, natural language processing, multitask learning
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Tariq, Dr Faisal
• Authors: Sohaib, M., Munir, S., Islam, M. M. M., Shin, J., Tariq, F., Ar Rashid, S. M. M., and Kim, J.-M.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Frontiers in Energy Research
• Publisher: Frontiers Media
• ISSN: 2296-598X
• ISSN (Online): 2296-598X
• Published Online: 07 September 2022
• Copyright Holders: Copyright © 2022 Sohaib, Munir, Islam, Shin, Tariq, Ar Rashid and Kim
• First Published: First published in Frontiers in Energy Research 10: 998760
• Publisher Policy: Reproduced under a Creative Commons License