Novel deep network-based transfer learning approach for fault detection of three-phase induction motor

Ibrahim, Alasmer, Anayi, Fatih ORCID: https://orcid.org/0000-0001-8408-7673 and Packianather, Michael ORCID: https://orcid.org/0000-0002-9436-8206 2022. Novel deep network-based transfer learning approach for fault detection of three-phase induction motor. Presented at: 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 28-29 April 2022. Proceedings of 2nd International Conference on Advance Computing and Innovative Technologies in Engineering. IEEE, pp. 655-659. 10.1109/ICACITE53722.2022.9823821

Full text not available from this repository.

Abstract

As induction motors in the industry operate in difficult and confined environments, condition monitoring is a critical topic to verify industry safety. This paper proposes a new hybrid application that uses a pre-trained model with machine learning classifiers for fault diagnosis using the induction motor thermal images. A deep learning system based on the EfficientNet-B0 model is developed as a feature extractor. The use of pre-trained model architecture does not need a manual attribute extraction, which can improve classification performance without a noticeable increase in computational complexity. The methodology of this work includes two steps: The first step is to develop a deep learning model as a feature extractor tool with imageNet based weight, while the second step considers the classification assessment. Thermal images have been applied to investigate the task of fault diagnosis. The achieved results have accurately proved the superiority and robustness of the proposed model. The combination of the proposed pre-trained network with a Random Forest classifier (RF) has achieved the highest accuracy of 97%. Moreover, this model might be used for other applications to detect the related class.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	IEEE
ISBN:	9781665437905
Last Modified:	30 Nov 2022 08:36
URI:	https://orca.cardiff.ac.uk/id/eprint/152044

Citation Data

Cited 1 time in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item