Formal verification of safety-critical systems with uncertainty for Industry 4.0 applications

Xin, Xin (2025) Formal verification of safety-critical systems with uncertainty for Industry 4.0 applications. PhD thesis, University of Glasgow.

Abstract

Industry 4.0 adopts Internet of Things (IoT) and service-oriented architectures to integrate Cyber-Physical Systems (CPSs) and Enterprise Planning systems into manufacturing operations. Furthermore, manufacturing processes typically involve the composition of various modular CPSs that work as a whole, such as multiple Collaborative Robots (cobots) working together as a production line, improving the production process’s flexibility and resilience. On the other hand, it is still challenging to verify this kind of compositional process and take into account uncertainties from IoT sensors and decision-making algorithms. For example, the trustworthiness of the sensors is essential to guarantee performance, safety and product quality during operation. However, existing methodologies to test such systems often do not scale to today’s sensor networks’ complexity and dynamic nature.

Formal model verification techniques are a valuable tool that allows strong reasoning about the high-level design of CPSs. However, the uncertainty exhibited by the underlying sensor networks is often ignored. Moreover, existing model-checking tools are hard to adapt to the dynamic environment of Industry 4.0 applications during the operation stage, such as an Automated Guided Vehicle (AGV) joining in accompanying the manufacturing process at run time.

This thesis proposes a novel run-time formal verification framework for modular CPSs that combines sensor-level data-driven fault detection and system-level probabilistic model checking. The resulting framework can quantify sensor readings’ trustworthiness, enabling formal reasoning for system operation behaviour and reliability analysis.

The proposed approach is evaluated on three use cases, including an industrial turn-mill machine equipped with a sensor network to monitor its main components continuously, a passenger lift with two sensor networks to monitor the door and cabinet car movements, and a two-cobot painting process running with Robotic Operation System (ROS). The results indicate that the proposed verification framework involving the quantified sensor’s trustworthiness enhances the accuracy of the system failure prediction and potentially optimises manufacturing processes.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Additional Information: This research is partially funded by Singapore Economic Development Board (EDB) through the Industrial Postgraduate Programme (IPP) Grant. Also, this research is supported by TÜV SÜD Asia Pacific Pte Ltd—Digital Service Singapore.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• Colleges/Schools: College of Science and Engineering > School of Computing Science
• Funder's Name: Singapore Economic Development Board (EDB)
• Supervisor's Name: Keoh, Dr. Sye Loong
• Date of Award: 2025
• Depositing User: Theses Team
• Unique ID: glathesis:2025-85121
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 16 May 2025 14:28
• Last Modified: 16 May 2025 14:28
• Thesis DOI: 10.5525/gla.thesis.85121
• URI: https://theses.gla.ac.uk/id/eprint/85121

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