Improved convolutional neural network chiller early fault diagnosis by gradient-based feature-level model interpretation and feature learning

For chillers, fault diagnosis (FD) is important for maintaining system reliability and performance. Deep learning methods, such as convolutional neural network (CNN), have been widely studied for chiller FD for its more significant diagnosis accuracy. But CNN model with deep layers and complex structures is black-box and difficult to interpret, which would greatly limit its practical FD applications for chillers. Traditional CNN model interpretation method may be not sensitive to interpret the chillers systematic faults especially at their early stages. Hence, to further obtain better interpretation of the CNN FD model, this study proposed a high-sensitivity gradient-based interpretation method. The proposed method adopts a softsign-forward-ReLU-backward manner to interpret the CNN model from the prospective of fault-discriminative feature, which localizes the fault-related feature variables and visualizing the diagnosis criteria for the CNN identified faults. The ASHRAE research project 1043 (RP-1043) chiller fault dataset was used to validate the proposed model interpretation and explanation method with higher feature-level sensitivity for the incipient fault. Based on the feature-level explanation and feature learning results, different feature combinations were investigated to improve diagnosis accuracy of some early-stage faults. If only small sizes of training data were available for modelling, 17 fault-related features were selected from the original 64 features to re-develop the CNN model and achieved diagnosis accuracy improvement of 9% for the early-stage improper refrigerant charge faults at most.