VIT-GADG: A Generative Domain-Generalized Framework for Chillers Fault Diagnosis Under Unseen Working Conditions

Jiang, Kexin; Gao, Xuejin; Gao, Huihui; Han, Huayun; Qi, Yongsheng

doi:10.1109/tim.2023.3304687

Cited by 10 publications

(1 citation statement)

References 46 publications

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“…To ensure that the domain augmentation module can generate data with sufficient distributional disparities, a variational autoencoder (VAE) is employed to amplify the feature distribution difference In order to eliminate the dependence on target domain data, fault diagnosis methods based on domain generalization (DG) have emerged as a new research focus. DG methods involve learning from multiple source domains, extracting domain-invariant features, and consequently constructing fault diagnosis models capable of generalizing to unknown target domains, as illustrated in Figure 1b [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

An Adversarial Single-Domain Generalization Network for Fault Diagnosis of Wind Turbine Gearboxes

Wang,

Liu

et al. 2023

JMSE

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In deep learning-based fault diagnosis of the wind turbine gearbox, a commonly faced challenge is the domain shift caused by differing operational conditions. Traditional domain adaptation methods aim to learn transferable features from the source domain and apply them to the target data. However, such methods still require access to target domain data during the training process, which limits their applicability in real-time fault diagnosis. To address this issue, we introduce an adversarial single-domain generalization network (ASDGN). It relies solely on data from a single length of data acquisition in wind turbine fault diagnosis. This novel approach introduces a more flexible and efficient solution to the field of real-time fault diagnosis for wind turbines.

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Section: Introductionmentioning

confidence: 99%