Data Logging and Reconstruction of Discrete-event System Behavior

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

In case of malfunctions or accidents related to an infrastructural system, it is useful to reconstruct and analyze the behavior that led to such an undesired situation. Understanding the behavior can help in improving the plant and the supervisory controller such that this situation is not encountered again. Many computer-controller mechanical systems use programmable logic controllers (PLCs) to implement the supervisory controller and to collect data from the system. Currently, incident analysis for PLCs often consists of plotting actuator and sensor signals to reconstruct and analyze the behavior. This way of analyzing is laborious and difficult to interpret for engineers not familiar with the system. In this paper, a different behavioral reconstruction and analysis method is proposed. In this method, models developed during the design of the supervisory controller are reused. From the collected data, a finite-state automaton is constructed. This automaton can be used for behavioral reconstruction via simulation, which is simpler and more intuitive. Moreover, by comparing the logged behavior with the behavior defined in the available models, faults can be identified. As a proof of concept, the behavior of a real movable bridge has been logged from a PLC, reconstructed, simulated, and analyzed.

Original languageEnglish
Title of host publication16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020
PublisherInstitute of Electrical and Electronics Engineers
Pages1020-1026
Number of pages7
ISBN (Electronic)9781728177090
DOIs
Publication statusPublished - 1 Aug 2021
Event16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020 - Virtual, Shenzhen, China
Duration: 13 Dec 202015 Dec 2020

Conference

Conference16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020
CountryChina
CityVirtual, Shenzhen
Period13/12/2015/12/20

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