Adaptive model verification for modularized industry 4.0 applications

Xin, X. , Keoh, S. L. , Sevegnani, M. , Saerbeck, M. and Khoo, T. P. (2022) Adaptive model verification for modularized industry 4.0 applications. IEEE Access, 10, pp. 125353-125364. (doi: 10.1109/ACCESS.2022.3225399)

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Cyber-Physical Systems (CPSs) are the core of Industry 4.0 applications, integrating advanced technologies such as sensing, data analytics, and artificial intelligence. This kind of combination typically consists of networked sensors and decision-making processes in which sensor-generated data drive the control decisions. Hence, the trustworthiness of the sensors is essential to guarantee performance, safety and quality during operation. Formal model verification techniques are a valuable tool allowing strong reasoning about the high-level design of CPSs. However, the uncertainty exhibited by the underlying sensor networks is often ignored. Manufacturing processes typically involve composition of various modular CPSs that work as a whole, such as multiple Collaborative Robots (cobots) working together as a production line, which improves the flexibility and resilience of the production process. It is still challenging to verify this class of compositional process while also considering uncertainty. We propose a novel verification framework for modular CPSs that combines sensor-level data-driven fault detection and system-level model-driven probabilistic model checking. The resulting framework can rigorously quantify sensor readings’ trustworthiness, enabling formal reasoning for system failure prediction and reliability analysis. We validated our approach on a cobots-based manufacturing process.

Item Type:Articles
Additional Information:The work of Xin Xin was supported in part by the Singapore Economic Development Board (EDB) through the Industrial Postgraduate Programme (IPP) Grant. The work of Michele Sevegnani was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/F033206/1, in part by the Formal Methods for Agritech Resilience Modelling (FARM) under Grant EP/S035362/1, in part by the Multi-Perspective Design of IoT Cybersecurity in Ground and Aerial Vehicles (MAGIC), and in part by the Amazon Research Award.
Glasgow Author(s) Enlighten ID:Keoh, Dr Sye Loong and Sevegnani, Dr Michele and Xin, Xin
Authors: Xin, X., Keoh, S. L., Sevegnani, M., Saerbeck, M., and Khoo, T. P.
Subjects:Q Science > QA Mathematics > QA76 Computer software
College/School:College of Science and Engineering > School of Computing Science
Research Centre:College of Science and Engineering > School of Computing Science > IDA Section
Journal Name:IEEE Access
ISSN (Online):2169-3536
Copyright Holders:Copyright © The Author(s) 2022
First Published:First published in IEEE Access 10:125353-125364
Publisher Policy:Reproduced under a Creative Commons license

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
143329Verifying interoperability requirements in Pervasive Systems (VPS)Muffy CalderEngineering and Physical Sciences Research Council (EPSRC)EP/F033206/1Computing Science