Towards Systematic Reliability Assessment: A Multi-Criteria Decision Framework for Modeling Heat Pump Systems
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Abstract
Reliability assessment is essential to ensure the performance, availability, and safety of heat pump systems. This requires modeling strategies that capture both component-level behavior and system-level interactions. A wide range of reliability modeling approaches exists—including physics-based, data-driven, and hybrid methods—each offering distinct strengths suited to specific operational conditions and system architectures. Modern heat pump systems introduce added complexity: technically, through tightly coupled components; and organizationally, through fragmented supply chains and varying supplier inputs. These factors lead to heterogeneous levels of physical insight across components—from well-understood to poorly characterized. In parallel, the growing adoption of IoT technologies enables operational data collection, though such data often remains unstructured and lacks consistent failure labeling. Together, these challenges hinder the integration of appropriate modeling strategies and create a practical gap in applied reliability methods. To address this, we present a structured, scalable, and adaptable multi-criteria decision-making framework for reliability modeling in complex heat pump systems. The framework begins with component prioritization at the system level, followed by a structured evaluation of risk components using four key indicators: forecast granularity, physical understanding, data availability, and cost efficiency. This decision process is demonstrated on a real-world air-to-water heat pump use case. The proposed approach provides practitioners with a systematic pathway for selecting reliability modeling strategies tailored to varying levels of system complexity, available resources, practitioner expertise, and data constraints.
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Reliability Modeling, Heat Pump Systems, Multi-Criteria Decision Making, Reliability Block Diagram, Component-Level Modeling, Component Prioritization, Physics-Based Approaches, Data-Driven Approaches, Hybrid Approaches
1080/0951192X.2023.2204471
Azam, M., Ghoshal, S., Deb, S., Pattipati, K., Haste, D., Mandal, S., & Kleinman, D. (2014). Integrated diagnostics and time to maintenance for complex engineering systems. In Proceedings of the IEEE Aerospace Conference (pp. 1–10). IEEE. https://hdl.handle.net/10945/44461
Brans, J. P., & Vincke, P. (1985). A preference ranking organisation method: (The PROMETHEE method for multiple criteria decision-making). Management Science, 31(6), 647–656. https://doi.org/
10.1287/mnsc.31.6.647
Brudermueller, T., Potthoff, U., Fleisch, E. (2025). Estimation of energy efficiency of heat pumps in residential buildings using real operation data. Nature Communications, 16, 2834. https://doi.org/10.1038
/s41467-025-58014-y
Doshi Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint, arXiv:1702.08608. https://doi.org/10.48550/
arXiv.1702.08608
Fischer, D., & Madani, H. (2017). On heat pumps in smart grids: A review. Renewable and Sustainable Energy Reviews, 70, 342–357. https://doi.org/10.1016/
j.rser.2016.11.182
Hasan, W., Ahmed, S., Tahar, S., & Hamdi, M. S. (2015). Reliability block diagrams based analysis: A survey. AIP Conference Proceedings, 1648(1), 850129. https://doi.org/10.1063/1.4913184
Hoang, T., & Kang, H.-J. (2018). A survey on deep learning based bearing fault diagnosis. Neurocomputing, 335., Article 403–420. https://doi.org/10.1016/j.neucom.
2018.06.078
International Energy Agency. Heat pumps. Paris, France: Author. Retrieved August 13, 2025, from https://www.iea.org/energy-system/buildings/heat-pumps
International Energy Agency. (2022). The future of heat pumps. Paris, France: Author. https://www.iea.org/
reports/the-future-of-heat-pumps
Ikwan, F., Sanders, D., & Haddad, M. (2020). A combined AHP–PROMETHEE approach for intelligent risk prediction of leak in a storage tank. International Journal of Reliability, Risk and Safety: Theory and Application, 3(2), 55–61. https://doi.org/10.30699/
IJRRS.3.2.7
Khan, A. U., & Ali, Y. (2020). Analytical hierarchy process (AHP) and analytic network process methods and their applications: A twenty-year review from 2000–2019. International Journal of the Analytic Hierarchy Process, 12(3). https://doi.org/10.13033/ijahp.v12i3.822
Khan, S., Yairi, T., Tsutsumi, S., & Nakasuka, S. (2024). A review of physics-based learning for system health management. Annual Reviews in Control, 57, 100932. https://doi.org/10.1016/j.arcontrol.2024.100932
Lazarova Molnar, S., Mohamed, N., & Shaker, H. R. (2017). Reliability modeling of cyber-physical systems: A holistic overview and challenges. In 2017 IEEE International Workshop on Measurement and Networking of Cyber-Physical Systems (MSCPES) (pp. 1–6). IEEE. https://doi.org/10.1109/MSCPES.
2017.8064536
Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23. https://doi.org/10.1016/j.mfglet.2014.12.001
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104, 799–836. https://doi.org/10.1016/j.ymssp.2017.11.016
Ma, Z., Jørgensen, B. N., & Ma, Z. G. (2024). A systematic data characteristic understanding framework towards physical-sensor big data challenges. Journal of Big Data, 11, 84. https://doi.org/10.1186/s40537-024-00942-5
Muhammad, D., Salman, M., Inan Keles, A., & Bendechache, M. (2025). All diagnosis: Can efficiency and transparency coexist? An explainable deep learning approach. Scientific Reports, 15. https://doi.org/
10.1038/s41598-025-97297-5
O’Connor, P. D. T., & Kleyner, A. (2012). Practical reliability engineering (5th ed.). Chichester, UK: Wiley.
Okoli, C., & Pawlowski, S. D. (2004). The Delphi method as a research tool: An example, design considerations, and applications. Information & Management, 42(1), 15–29. https://doi.org/10.1016/j.im.2003.11.002
Qarqour, A., Arora, S. J., Heisenberg, G., Rabe, M., & Kleinert, T. (2024). Utilizing data analysis for optimized determination of the current operational state of heating systems. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KDIR) (pp. 200–209). SciTePress. https://doi.org/10.5220/
0012876200003838
Saaty, T., Vargas, L., & St, C. (2022). The analytic hierarchy process. Springer.
Wang, Y., Zhao, Y., & Addepalli, P. (2019). Remaining useful life prediction using deep learning approaches: A review. Procedia Manufacturing, 49, 67–73. https://doi.org/10.1016/j.promfg.2020.06.015
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237. https://doi.org/10.1016/
j.ymssp.2018.05.050
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