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Computational Intelligence and Neuroscience
Volume 2016, Article ID 1652475, 30 pages
Research Article

PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems

1Unified Digital Manufacturing Laboratory, Department of Industrial Engineering, Ajou University, Suwon 443-749, Republic of Korea
2Department of Industrial Engineering, Ajou University, Suwon 443-749, Republic of Korea

Received 28 January 2016; Revised 22 April 2016; Accepted 9 May 2016

Academic Editor: Cheng-Jian Lin

Copyright © 2016 Arup Ghosh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Operational faults and behavioural anomalies associated with PLC control processes take place often in a manufacturing system. Real time identification of these operational faults and behavioural anomalies is necessary in the manufacturing industry. In this paper, we present an automated tool, called PLC Log-Data Analysis Tool (PLAT) that can detect them by using log-data records of the PLC signals. PLAT automatically creates a nominal model of the PLC control process and employs a novel hash table based indexing and searching scheme to satisfy those purposes. Our experiments show that PLAT is significantly fast, provides real time identification of operational faults and behavioural anomalies, and can execute within a small memory footprint. In addition, PLAT can easily handle a large manufacturing system with a reasonable computing configuration and can be installed in parallel to the data logging system to identify operational faults and behavioural anomalies effectively.