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)

Abstract

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.
• Status: Published
• Refereed: Yes
• 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
• Publisher: IEEE
• ISSN: 2169-3536
• 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

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
143329		Verifying interoperability requirements in Pervasive Systems (VPS)	Muffy Calder	Engineering and Physical Sciences Research Council (EPSRC)	EP/F033206/1	Computing Science