Towards Systematic Reliability Assessment: A Multi-Criteria Decision Framework for Modeling Heat Pump Systems

Reliability assessment is critical to ensure the performance, availability, and safety of heat pump systems. This requires modeling strategies that reflect both component-level behavior and system-wide interactions. While physics-based, data-driven, and hybrid methods each offer unique strengths, selecting the right approach remains unsolved. This is especially evident in modern heat pump systems with tightly coupled components, fragmented supply chains, and heterogeneous levels of physical insight. Although IoT adoption enables operational data collection, such data remains often unstructured and lacks failure labeling, which limits its value for modeling. These challenges highlight the need for structured guidance in selecting suitable reliability strategies. To address this, a structured and scalable decision framework is proposed to support transparent, context-aware reliability modeling. The approach begins with system-level risk prioritization and applies five Key Decision Indicators (KDIs) to assign appropriate modeling or estimation strategies for each component. This includes both in-depth modeling for risk components and simplified estimation for passive ones. Applied to a real-world air-to-water heat pump system, the framework enables traceable modeling decisions adapted to data availability, physical knowledge, forecasting needs, and cost-efficiency. It offers practitioners a systematic pathway to tailor reliability modeling across complex systems and constrained development environments.