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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.