A semi-supervised approach to fault detection and diagnosis for building HVAC systems based on the modified generative adversarial network

• Main Authors: Li, Bingxu, Cheng, Fanyong, Cai, Hui, Zhang, Xin, Cai, Wenjian
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Electrical and electronic engineering; Imbalanced Learning; Fault Detection and Diagnosis
• Online Access: https://hdl.handle.net/10356/160416
• Institution: Nanyang Technological University

Developing efficient fault detection and diagnosis (FDD) techniques for building HVAC systems is important for improving buildings’ reliability and energy efficiency. The existing FDD methods can achieve satisfying results only if there are sufficient labeled training data. However, labelling the da...