Abstract :
Cyber-physical systems (CPS) require a reliability theory that is broader than the classical probability of failure-free operation of a technical component. In CPSs, failure can be caused by deterioration, sensor error, delay in communication, software malfunction, control problems, cyber vulnerabilities, human interaction, and stress due to environment. This paper offers a mathematical approach to CPS reliability that brings together classical theories of reliability with multilayer, state-dependent, logical, Bayesian, and predictive approaches. The result is an integrated model in which the exponential and Weibull life distributions, structural reliability approaches, Markov models of state transitions, fault trees, Bayesian inference, normalization, and multilayer integral approach to reliability are combined into one coherent methodology. The paper presents some extended concepts of reliability, such as availability, maintainability, resilience, recoverability, data integrity, and CPS safety. The proposed approach makes possible theoretical work and practical decisions, since it connects layer-by-layer indicators with layer indices, layer indices with system reliability, and system reliability with failure probability prediction.
Keywords :
cyber-physical systems; reliability; Weibull model; Markov model; fault tree analysis; Bayesian updating; availability; predictive maintenance; MINKFS; DKPN.References :
- Alur, R. (2015). Principles of cyber-physical systems. MIT Press. https://doi.org/10.5555/2774947
- Aven, T. (2016). Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research, 253(1), 1–13. https://doi.org/10.1016/j.ejor.2015.12.023
- Birolini, A. (2017). Reliability engineering: Theory and practice (8th ed.). Springer. https://doi.org/10.1007/978-3-662-54209-5
- Flammini, F. (Ed.). (2019). Resilience of cyber-physical systems: From risk modelling to threat counteraction. Springer. https://doi.org/10.1007/978-3-319-95597-1
- Friederich, J., & Lazarova-Molnar, S. (2021). Towards data-driven reliability modeling for cyber-physical production systems. Procedia Computer Science, 184, 589–596. https://doi.org/10.1016/j.procs.2021.03.073
- Griffor, E. R., Greer, C., Wollman, D. A., & Burns, M. J. (2017). Framework for cyber-physical systems: Volume 1, overview (NIST Special Publication 1500-201). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.1500-201
- Hollnagel, E. (2014). Safety-I and Safety-II: The past and future of safety management. Ashgate. https://doi.org/10.1201/9781315607511
- Humayed, A., Lin, J., Li, F., & Luo, B. (2017). Cyber-physical systems security—A survey. IEEE Internet of Things Journal, 4(6), 1802–1831. https://doi.org/10.1109/JIOT.2017.2703172
- Kabir, S. (2017). An overview of fault tree analysis and its application in model-based dependability analysis. Expert Systems with Applications, 77, 114–135. https://doi.org/10.1016/j.eswa.2017.01.058
- Lee, E. A. (2008). Cyber physical systems: Design challenges. In 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC) (pp. 363–369). IEEE. https://doi.org/10.1109/ISORC.2008.25
- Lee, E. A., & Seshia, S. A. (2017). Introduction to embedded systems: A cyber-physical systems approach (2nd ed.). MIT Press. https://doi.org/10.5555/3086978
- Leveson, N. G. (2012). Engineering a safer world: Systems thinking applied to safety. MIT Press. https://doi.org/10.7551/mitpress/8179.001.0001
- Marwedel, P. (2021). Embedded system design: Embedded systems foundations of cyber-physical systems, and the Internet of Things (4th ed.). Springer. https://doi.org/10.1007/978-3-030-60910-8
- National Institute of Standards and Technology. (2024). The NIST Cybersecurity Framework (CSF) 2.0 (NIST CSWP 29). https://doi.org/10.6028/NIST.CSWP.29
- Nikolov, N. (2026). A KPI-based model for smart management of urban green infrastructure and ecosystem services. Forestry Ideas, 32(1), 209–222.
- Nikolov, N. (2025). Intelligent Urban Systems and Industry 5.0: Creating Adaptive Ecosystems for SustainableEnergyand Resource Management. International Journal of Current Science Research and Review, 8(1), 103-115,DOI:https://doi.org/10.47191/ijcsrr/V8-i1-11
- Nikolov, N. (2024). Development of an Intelligent System for Managing Energy Consumption in the Home. ComputerScience and Technologies (Technical University of Varna), ISSN 1312-3335, pp. 11-20.
- Rajkumar, R., Lee, I., Sha, L., & Stankovic, J. (2010). Cyber-physical systems: The next computing revolution. In Proceedings of the 47th Design Automation Conference (pp. 731–736). Association for Computing Machinery. https://doi.org/10.1145/1837274.1837461
- Rausand, M., & Høyland, A. (2004). System reliability theory: Models, statistical methods, and applications (2nd ed.). Wiley. https://doi.org/10.1002/9780470316900
- Ruijters, E., & Stoelinga, M. (2015). Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools. Computer Science Review, 15–16, 29–62. https://doi.org/10.1016/j.cosrev.2015.03.001
- Trivedi, K. S. (2016). Probability and statistics with reliability, queuing, and computer science applications. Wiley. https://doi.org/10.1002/9781119285441
- Weber, P., Medina-Oliva, G., Simon, C., & Iung, B. (2012). Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Engineering Applications of Artificial Intelligence, 25(4), 671–682. https://doi.org/10.1016/j.engappai.2010.06.002
- Zio, E. (2016). Some challenges and opportunities in reliability engineering. IEEE Transactions on Reliability, 65(4), 1769–1782. https://doi.org/10.1109/TR.2016.2591504

