Autonomous Smart-Edge Fault Diagnostics via Edge-Cloud-Orchestrated Collaborative Computing for Infrared Electrical Equipment Images

Current methodologies in electrical equipment fault diagnosis are not based on an edge-cloud computing architecture; however, the potential applicability of this architecture for realizing real-time autonomous AI-based fault diagnosing is vast. Collaborative cloud and edge computing brings data computation and storage closer to Internet of Things (IoT) data sources; specifically, data are processed at the periphery of the network (the Internet). In this article, an intelligent fault diagnosis methodology based on infrared thermal imaging (infrared thermography (IRT) images) and deep learning (DL) in an edge-cloud collaborative computing architecture is developed for smart electrical equipment fault diagnosis. In the developed methodology, YOLOv8 is used to perform object detection and classification of objects in captured IRT images of electrical components for on-site and on-line condition monitoring. This algorithm is implemented and run on an nVIDIA
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Jetson Nano™ developer kit to serve as an autonomous edge AI device in the architecture. The Jetson Nano™ kit is integrated with an FLIR Lepton
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3.5 thermal imaging module to capture accurate, calibrated and noncontact temperature data in every pixel of each IRT image. The developed architecture and its preliminary implementation are described and tested. Additionally, the
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and performance evaluation are shown. As reported, via the proposed methodology, a performance improvement of ~20% in the mAP@.5 in object detection and classification is achieved for an addressed task as an illustrative paradigm of the developed architecture. Additionally, a reduction of ~83% of the bandwidth of the backbone network is gained. Ultimately, the developed architecture may be applied to IRT-based intelligent fault diagnosis in other power equipment in the area of power equipment diagnosis.