Fault Detection and Isolation in Industrial Processes Using Deep Learning Approaches

Rahat Iqbal; Tomasz Maniak; Faiyaz Doctor; Charalampos Karyotis

doi:10.1109/TII.2019.2902274

Fault Detection and Isolation in Industrial Processes Using Deep Learning Approaches

Rahat Iqbal, Tomasz Maniak, Faiyaz Doctor, Charalampos Karyotis

Research output: Contribution to journal › Article › peer-review

103 Citations (Scopus)

319 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 language	English
Article number	8654684
Pages (from-to)	3077 - 3084
Number of pages	8
Journal	IEEE Transactions on Industrial Informatics
Volume	15
Issue number	5
Early online date	28 Feb 2019
DOIs	https://doi.org/10.1109/TII.2019.2902274
Publication status	Published - 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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10.1109/TII.2019.2902274

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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.",

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AU - Karyotis, Charalampos

N1 - © 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.

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Fault Detection and Isolation in Industrial Processes Using Deep Learning Approaches

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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