Semi-supervised CWGAN-GP modeling for AHU AFDD with high-quality synthetic data filtering mechanism

Abstract

Supervised learning methods demonstrated high classification accuracy for air handling unit (AHU) automated fault detection and diagnosis (FDD) scenarios with well-shaped training datasets. However, for imbalanced training datasets, i.e., much less real-world fault training data samples against an enormous amount of normal data samples, the supervised learning-based methods failed to produce satisfactory FDD results. To address the above-mentioned issue, this study proposes a semi-supervised conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) to generate high-quality synthetic fault training samples. The semi-supervised learning-based AHU AFDD framework is completed by identifying high-quality synthetic fault samples and inserting them into the training pool iteratively. With different numbers of real-world fault samples, comparative experiments are conducted on datasets collected by ASHRAE project RP-1312 in the summer and winter seasons. The experimental results show that the proposed AFDD method has obvious advantages over the traditional method with limited numbers of real-world fault samples. Moreover, the proposed CWGAN-GP-SSL framework achieves superior AFDD performance compared to the existing GAN-based AHU AFDD method.