Interpretable chiller fault diagnosis based on physics-guided neural networks

Abstract

Chillers are critical for building system energy efficiency and occupant comfort. Various methods have been developed for diagnosing chiller faults, with deep learning being a common choice due to its strong end-to-end learning capability. But in the absence of physical knowledge, deep learning models cannot provide a diagnosis outcome in accordance with the mechanisms behind faults, resulting in models lacking interpretability. In this regard, this paper proposes an interpretable chiller fault diagnosis method based on physics-guided neural networks. Through a comprehensive analysis of the chiller’s working process and fault evolution, a key physical indicator related to the condenser fouling fault is derived. Then a corresponding physical inconsistency loss function is developed to construct a condenser fouling fault-oriented diagnostic model. The proposed method has been demonstrated to be effective in reducing physical inconsistency while retaining diagnostic performance. The proposed method is conducive to applying the neural network approach in real operational scenarios and can serve as a reference for future research.