Digital twin-based fault tolerance approach for Cyber-Physical Production System.

Autor: Saraeian S; Department of Computer Engineering, Gorgan Branch, Islamic Azad University, Gorgan, Iran. Electronic address: shidehsaraeian@gorganiau.ac.ir., Shirazi B; Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran.
Jazyk: angličtina
Zdroj: ISA transactions [ISA Trans] 2022 Nov; Vol. 130, pp. 35-50. Date of Electronic Publication: 2022 Mar 16.
DOI: 10.1016/j.isatra.2022.03.007
Abstrakt: Cyber-Physical Production Systems (CPPSs) as distributed Systems of Systems (SoS) are at the center of attention from different industries. CPPSs face different categories of errors. These errors will cause failures of the entire production chain. To handle this concern, production systems should be converted into fault-tolerant production systems. To present such systems, a fault tolerance approach was developed to help possible faults prediction and detection of faults causes in this study. Also, the increasing complexity and uncertainty of CPPS call for Digital Twin (DT)-based fault tolerance approach. The proposes approach uses an extraction module to extract the faults signatures efficiently. Based on all extracted faults, appropriate responses could be generated through reliable faults patterns prediction. This method is provided using Fault Tree Analyzer (FTA), Zero-suppressed Decision Diagram (ZDD), and Support Vector Machine-Adaptive Neuro-Fuzzy Inference System (SVM-ANFIS) structure. The results based on digital twin-based CPPS of the food production system as a use case show that the proposed approach can predict reliable faults signatures to prevent failures and make a much reliable production system. Also, this method can guarantee that CPPS is up and running with optimal levels at all times.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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