Fast and Accurate Deep Learning Framework for Secure Fault Diagnosis in the Industrial Internet of Things
Peer reviewed, Journal article
MetadataShow full item record
Original versionDjenouri, Y., Belhadi, A., Srivastava, G., Ghosh, U., Chatterjee, P., & Lin, J. C.-W. (2021). Fast and accurate deep learning framework for secure fault diagnosis in the industrial internet of things. IEEE Internet of Things Journal. 10.1109/JIOT.2021.3092275
This paper introduced a new deep learning framework for fault diagnosis in electrical power systems. The framework integrates the convolution neural network and different regression models to visually identify which faults have occurred in electric power systems. The approach includes three main steps, data preparation, object detection, and hyper-parameter optimization. Inspired by deep learning, evolutionary computation techniques, different strategies have been proposed in each step of the process. In addition, we propose a new hyper-parameters optimization model based on evolutionary computation that can be used to tune parameters of our deep learning framework. In the validation of the framework’s usefulness, experimental evaluation is executed using the well known and challenging VOC 2012, the COCO datasets, and the large NESTA 162-bus system. The results show that our proposed approach significantly outperforms most of the existing solutions in terms of runtime and accuracy.
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.