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

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.