How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method

Abstract

Automated fault detection and diagnosis (AFDD) plays a crucial role in enhancing the energy efficiency of air conditioning systems. Deep learning has emerged as a promising tool for image classification, and its application in the context of AFDD of HVAC systems is gaining traction due to its exceptional performance. However, the deployment cost of deep models in practical scenarios is increased due to the large number of parameters and the lack of interpretability. This paper focuses on improving the potential of deep learning models for AFDD in real HVAC systems. We use pruning to reduce the number of parameters in the model and use layer-wise relevance propagation (LRP) to improve the interpretability of the model. The case study builds a simulation model and 31 kinds of fault data sets based on the actual HVAC in Japan. Based on the findings, Without loss of accuracy, the pruning method can reduce the model size by more than 99 % and maintain 90% classification accuracy. The LRP score allows model users to find out the input data that most affects the results at each diagnosis, improving interpretability.