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Fault Detection and Isolation in Industrial Processes Using Deep Learning Approaches

Iqbal, Rahat and Maniak, Tomasz and Doctor, Faiyaz and Karyotis, Charalampos (2019) 'Fault Detection and Isolation in Industrial Processes Using Deep Learning Approaches.' IEEE Transactions on Industrial Informatics, 15 (5). 3077 - 3084. ISSN 1551-3203

IEEE TII Fault-Detection-Isolation-Paper_fdocto.pdf - Accepted Version

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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 non-linear signals is performed manually by trained operators, whose knowledge is used to supervise quality checking and manual detection of faults. In this paper, a novel approach for automated fault detection and isolation based on deep machine learning techniques is presented. The approach was tested on data generated by computer-based manufacturing systems equipped with local and remote sensing devices. The results show that the proposed approach models the different spatial / temporal patterns found in the data. The approach is also able to successfully diagnose and locate multiple classes of faults under real-time working conditions. The proposed method is shown to outperform other established fault detection and isolation methods.

Item Type: Article
Uncontrolled Keywords: Artificial neural networks, Computer aided manufacturing, Fault detection, Machine learning, Manufacturing automation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 23 Apr 2019 15:36
Last Modified: 20 May 2019 14:15

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