A GAN-enhanced transfer learning framework for cross-condition chiller fault diagnosis under severe data scarcity

Yao, Kaitian, Zhao, Tianyi, Jin, Tao, Xiao, Manxuan, Zhang, Xingxing, Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400 and Li, Yu 2026. A GAN-enhanced transfer learning framework for cross-condition chiller fault diagnosis under severe data scarcity. Building and Environment 303 , 114926. 10.1016/j.buildenv.2026.114926

PDF - Accepted Post-Print Version Available under License Creative Commons Attribution. Download (2MB)

Abstract

Data-driven fault detection and diagnosis (FDD) for chiller systems is often constrained by the severe scarcity of labeled fault data under practical operating conditions, which significantly degrades model robustness and generalization performance. To address this challenge, this study presents a GAN-enhanced transfer learning framework for cross-condition chiller fault diagnosis, integrating synthetic sample generation, high-quality sample filtering, target-domain data augmentation, and cross-condition diagnosis into a unified workflow. Specifically, an auxiliary classifier Wasserstein GAN with gradient penalty (ACWGAN-GP) is employed to generate labeled fault samples in the target domain, while an adaptive weight dual-classifier fusion mechanism is designed to filter high-quality synthetic data. The filtered samples are then incorporated into both fine-tuning (FT) and domain-adversarial neural network (DANN) pipelines to improve diagnostic performance under severe data scarcity. Experimental results show that the proposed filtering mechanism improves fault diagnosis performance of the baseline model by approximately 10% when only 1/7 of real fault data are available. In cross-condition scenarios with a target-domain data ratio as low as 1/20, the proposed method achieves additional performance gains of approximately 2%-8% over conventional transfer learning methods. The results indicate that the proposed framework offers an effective solution for enhancing data-driven fault diagnosis performance in building energy systems under severely limited data conditions.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Additional Information:	RRS policy applied
Publisher:	Elsevier
ISSN:	0360-1323
Date of First Compliant Deposit:	30 June 2026
Date of Acceptance:	25 June 2026
Last Modified:	30 Jun 2026 09:30
URI:	https://orca.cardiff.ac.uk/id/eprint/187817

Actions (repository staff only)

Edit Item