Can a deep learning based IoT fault diagnosis system identify more than one fault at a time?

Alireza Salimy, Imene Mitiche, Philip Boreham, Alan Nesbitt, Gordon Morison

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages19-24
Number of pages6
ISBN (Electronic)9798350396454
ISBN (Print)9798350396461
DOIs
Publication statusPublished - 13 Dec 2022
Event2022 IEEE International Conference on Internet of Things and Intelligence Systems - Online
Duration: 24 Nov 202226 Nov 2022

Publication series

NameProceedings of the 2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022
ISSN (Print)2832-1375
ISSN (Electronic)2832-1383

Conference

Conference2022 IEEE International Conference on Internet of Things and Intelligence Systems
Abbreviated titleIoTaIS 2022
Period24/11/2226/11/22

Keywords

  • condition monitoring
  • EMI technique
  • high-voltage asset diagnosis
  • Multi-label classification

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Modelling and Simulation
  • Artificial Intelligence

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