Fault Detection and Isolation in Industrial Processes Using Deep Learning Approaches

Rahat Iqbal, Tomasz Maniak, Faiyaz Doctor, Charalampos Karyotis

Research output: Contribution to journalArticle

8 Citations (Scopus)
43 Downloads (Pure)

Abstract

Automated fault detection is an important part of a quality control system. It has the potential to increase the overall quality of monitored products and processes. The fault detection of automotive instrument cluster systems in computer-based manufacturing assembly lines is currently limited to simple boundary checking. The analysis of more complex nonlinear signals is performed manually by trained operators, whose knowledge is used to supervise quality checking and manual detection of faults. We present a novel approach for automated Fault Detection and Isolation (FDI) based on deep learning. The approach was tested on data generated by computer-based manufacturing systems equipped with local and remote sensing devices. The results show that the approach models the different spatial/temporal patterns found in the data. The approach can successfully diagnose and locate multiple classes of faults under real-time working conditions. The proposed method is shown to outperform other established FDI methods.

Original languageEnglish
Article number8654684
Pages (from-to)3077 - 3084
Number of pages8
JournalIEEE Transactions on Industrial Informatics
Volume15
Issue number5
Early online date28 Feb 2019
DOIs
Publication statusPublished - 1 May 2019

Bibliographical note

© 2019 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.

Keywords

  • Artificial Neural Networks (ANNs)
  • computer-aided manufacturing
  • deep learning
  • fault detection
  • machine learning
  • manufacturing automation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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