New Transfer Learning Approach Based on a CNN for Fault Diagnosis

Ibrahim, Alasmer, Anayi, Fatih ORCID: https://orcid.org/0000-0001-8408-7673 and Packianather, Michael ORCID: https://orcid.org/0000-0002-9436-8206 2022. New Transfer Learning Approach Based on a CNN for Fault Diagnosis. Presented at: 1st International Electronic Conference on Machines and Applications, 15–30 September 2022, Online, 15-30 September 2022. The 1st International Electronic Conference on Machines and Applications. Engineering Proceedings. (16) MDPI, 10.3390/IECMA2022-12905

PDF - Published Version Available under License Creative Commons Attribution. Download (2MB)

Abstract

Induction motors operate in difficult environments in the industry. Monitoring the performance of motors in such circumstances is significant, which can provide a reliable operation system. This paper intends to develop a new model for fault diagnosis based on the knowledge of transfer learning using the ImageNet dataset. The development of this framework provides a novel technique for the diagnosis of single and multiple induction motor faults. A transfer learning model based on a VGG-19 convolutional neural network (CNN) was implemented, which provided a quick and fast training process with higher accuracy. Thermal images with different induction motor conditions were captured with the help of an FLIR camera and applied as inputs to investigate the proposed model. The implementation of this task involved the use of a VGG-19 CNN-based pre-trained network, which provides autonomous features learning based on minimum human intervention. Next, a dense-connected classifier was applied to predict the true class. The experimental results confirmed the robustness and reliability of the developed technique, which was successfully able to classify the induction motor faults, achieving a classification accuracy of 99.8%. The use of a VGG-19 network allowed the attributes to be automatically extracted and associated with the decision-making part. Furthermore, this model was further compared with other applications based on related topics; it successfully proved its superiority and robustness.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	MDPI
ISSN:	2673-4591
Date of First Compliant Deposit:	9 January 2023
Last Modified:	09 Jan 2023 14:45
URI:	https://orca.cardiff.ac.uk/id/eprint/155407

Actions (repository staff only)

Edit Item