A Physics-Informed Explainable AI Framework for HVAC Anomaly Detection and Maintenance-Oriented Analysis in Urban Bus Fleets

Although urban bus fleets increasingly generate HVAC operational data through onboard sensors and telematics platforms, their practical use for maintenance decision-making remains limited. In real-world conditions, these data are rarely accompanied by reliable fault labels, complete contextual information, or a clear physical interpretation of the monitored variables. As a result, there is still a need for methodological frameworks capable of transforming imperfect operational data into physically meaningful and maintenance-oriented insights. This work proposes a structured framework that combines domain knowledge, unsupervised anomaly detection, and explainable artificial intelligence to support the initial exploitation of HVAC fleet data. The framework begins with sensor contextualization and physical mapping of available measurements onto the vapor-compression refrigeration cycle, enabling the construction of physically meaningful features and a coherent operational baseline. An Isolation Forest model is then used to identify deviations from this baseline, while Kernel SHAP explains the resulting deviation scores. SHAP contributions are further grouped into physics-informed explanation families, allowing anomalies to be interpreted in terms of HVAC mechanisms rather than isolated variables. The results show that this domain knowledge–XAI approach helps distinguish physically plausible rare operating states, inspection candidates, and ambiguous multivariate cases without relying on confirmed fault labels. The main contribution of this study is a physics-informed XAI framework that combines domain knowledge, unsupervised anomaly detection, and explainability methods to transform imperfect HVAC operational data from early-stage digitalized bus fleets into interpretable and maintenance-relevant information.