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

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

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...

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Bibliographic Details
Main Authors: Li, Bingxu, Cheng, Fanyong, Cai, Hui, Zhang, Xin, Cai, Wenjian
Other Authors: School of Electrical and Electronic Engineering
Format: Journal 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

Internet

https://hdl.handle.net/10356/160416

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