@inproceedings{947f724e79734fa0b28479c41bb0d228,
title = "Can a deep learning based IoT fault diagnosis system identify more than one fault at a time?",
abstract = "The experiments in this study propose a fault diagnosis method to incorporate in an internet-of-things (IoT) system for the condition monitoring of high-voltage generating stations. The approach is based on feature extraction with signal processing methods and a deep learning model to tackle fault classification in measured signals that contain one or more faults simultaneously. The proposed system implements feature extraction through the short-time Fourier transform (STFT) of 1-D electro-magnetic interference (EMI) fault signals obtained from online high-voltage (HV) assets. The produced feature maps are then used in parallel with label word embeddings to train and test a deep learning model consisting of, a graph convolutional network (GCN), implemented to learn inter-dependant fault label relationships from label co-occurrence matrices and label word embeddings, and a convolutional neural network (CNN) to extract relevant features from STFT data representations. The proposed system tackles the under-addressed EMI multi-label HV fault diagnosis problem and produces strong results in label classification even when implemented on a heavily imbalanced data set, to the author's knowledge the system provides an unprecedented level of performance that is industrially acceptable in fault diagnosis and can be successfully implemented on a real-world IoT-based condition monitoring system. In addition, in theory the proposed system is scalable for the prediction of a higher quantity of fault labels present in data instances.",
keywords = "condition monitoring, EMI technique, high-voltage asset diagnosis, Multi-label classification",
author = "Alireza Salimy and Imene Mitiche and Philip Boreham and Alan Nesbitt and Gordon Morison",
year = "2022",
month = dec,
day = "13",
doi = "10.1109/IoTaIS56727.2022.9976013",
language = "English",
isbn = "9798350396461",
series = "Proceedings of the 2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "19--24",
booktitle = "2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)",
address = "United States",
note = "2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022 ; Conference date: 24-11-2022 Through 26-11-2022",
}