A novel solution for data uncertainty and insufficient in data-driven chiller fault diagnosis based on multi-modal data fusion

Abstract

Accurate fault diagnosis of chillers is essential for extending equipment lifespan and reducing energy consumption. Currently, data-driven diagnostic models for chillers exhibit impressive performance. However, the outstanding performance is only guaranteed in condition of sufficient and high-quality data, i.e., data is often uncertain and insufficient. To resolve this problem, this study proposes a novel multimodal co-learning framework based on infrared thermography (IRT) and operational state parameters. State parameters, referred as evidence source 1, undergo data augmentation using conditional Wasserstein generative adversarial networks (CWGAN) before classification by a base classifier. IRTs referred as evidence source 2, are enhanced through a method called self-attention BAGAN with gradient penalty (SA-BAGAN-GP). Self-attention mechanisms is integrated in the encoder layer to capture critical features to produce high-quality samples. Then, generated IRT samples are then classified using the self-attention convolutional neural network (SA-CNN) model. Finally, Dempster-Shafer (D-S) evidence theory is utilized for the fusion of decision information from both modalities. By simultaneously capturing and integrating data from diverse sources, the model improves generalization and robustness. Experimental validation conducted on actual chillers demonstrated an average accuracy of 92.75% across four cross-condition tasks, with noise test accuracy ranging from 89.2% to 99.6% and outlier test accuracy between 98.5% and 99.4%.