Cyber-physical systems must meet high RAMS—reliability, availability, maintainability, and safety—standards. It is of essence to implement robust maintenance policies that decrease system downtime in a cost-effective way. Power plants and smart buildings are prominent examples where the cost of periodic inspections is high, and should be mitigated without compromising system reliability and availability. Fault Maintenance Trees (FMTs), a novel extension in fault tree analysis, can be used to assess system resilience: FMTs allow reasoning about failures in the presence of maintenance strategies, by encoding fault modes in a comprehensible and "maintenance-friendly" manner. A main concern is how to build a concrete model from the FMT, in order to compute the relevant RAMS metrics via (ideally automatic) analyses. Formal methods offer automated and trustworthy techniques to tackle with such task. In this work, we apply quantitative model checking—a well established formal verification technique—to analyse the FMT of a Heating, Ventilation and Air-Conditioning unit from a smart building. More specifically, we model the FMT in terms of continuous-time Markov
chains and priced time automata, which we respectively analyse using probabilistic and statistical model checking. In this way we are capable of automatically estimating the reliability, availability, expected number of failures, and differentiated costs of the FMT model for various time horizons and maintenance policies. We further contrast the two approaches we use, and identify their advantages and drawbacks.
How to Cite
Fault Maintenance Trees, Reliability, Availability, Maintenance, Model Checking, Probabilistic Model Checking, Statistical Model Checking, Smart Buildings, HVAC
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.