VIT-GADG: A Generative Domain-Generalized Framework for Chillers Fault Diagnosis Under Unseen Working Conditions
Kexin Jiang, Xuejin Gao, Huihui Gao, Huayun Han, Yongsheng Qi
Abstract
The extreme unbalance of training samples among different working conditions caused by complex and variable external environment makes the fault diagnosis of chiller based on domain adaptation (DA) poor performance. Although recently emerging fault diagnosis methods based on domain generalization (DG) can learn domain-invariant knowledge from multiple source domains and can generalize to unseen target domains, these methods still rely on multiple similar source domain data and rarely consider how to enhance the ability to distinguish the joint distribution of features extracted from the source domain samples. To address these problems, a generative domain-generalized framework for chillers fault diagnosis, namely, vision transformer generative adversarial domain generalization (VIT-GADG), is proposed. In VIT-GADG, a novel VIT domain generation network (VIT-DGN) is firstly designed to reduce DG’s dependence on multi-source domain data by improving the diversity of the distribution of the source domain samples. Then, a new adversarial domain generalization network called VIT conditional adversarial domain generalization network (VIT-CADGN) is designed to extract domain-invariant knowledge from source domain and latent domains that can be generalized to unseen target domains. Specifically, VIT module can effectively extract the global statistical feature of input samples, which is conducive to the identification of joint distribution. Simultaneously, the collaborative conditional domain discrimination strategy is introduced to improve the distribution discrimination ability of the extracted global statistical features while simultaneously aligning its conditional distribution. In addition, a personalized adaptive weight strategy is proposed to improve the performance of VIT-CADGN. Finally, the comprehensive case study shows that VIT-GADG has a satisfactory ability to extract invariant features, which improves the diagnosis accuracy in the unseen target domain.