MULTI-CLASSIFIER OPEN ADVERSARIAL NETWORK-BASED FAULT DIAGNOSIS METHOD FOR WIND TURBINE ROLLING BEARINGS

Hu Qinyi; Deng Aidong; Zhou Zhongzhi; Xiao Kaiwen; Shen Yang; Wu Yifan

doi:10.19912/j.0254-0096.tynxb.2025-0541

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (8) : 694-703. DOI: 10.19912/j.0254-0096.tynxb.2025-0541

MULTI-CLASSIFIER OPEN ADVERSARIAL NETWORK-BASED FAULT DIAGNOSIS METHOD FOR WIND TURBINE ROLLING BEARINGS

Hu Qinyi^1,2, Deng Aidong^1,2, Zhou Zhongzhi^1,2, Xiao Kaiwen^1,2, Shen Yang^1,2, Wu Yifan^1,2

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Abstract

To address the challenges of data distribution shift and unknown fault identification in wind turbine rolling bearings under varying operating conditions, an open-set fault diagnosis method based on a multi-classifier open adversarial network （MCOAN） is proposed. Specifically, a K-class and a （K+1）-class classifier are jointly employed within an adversarial domain adaptation framework to evaluate the similarity between target and source domain samples. Based on these similarity measures, a dynamic weighting mechanism is constructed to achieve adaptive sample weighting during open-set adversarial training. This mechanism also facilitates the dynamic determination of decision thresholds for known and unknown classes, thereby promoting cross-domain alignment of shared features while enhancing the recognition of unknown samples. In addition, a non-adversarial classifier is introduced to improve the reliability of dynamic weight computation. Experimental results on two datasets demonstrate that the proposed method achieves accurate alignment of shared features and robust identification of unknown faults, showcasing superior diagnostic performance and robustness.

Key words

wind turbines / fault diagnosis / rolling bearings / adversarial training / domain adaptation / open set

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Hu Qinyi, Deng Aidong, Zhou Zhongzhi, Xiao Kaiwen, Shen Yang, Wu Yifan. MULTI-CLASSIFIER OPEN ADVERSARIAL NETWORK-BASED FAULT DIAGNOSIS METHOD FOR WIND TURBINE ROLLING BEARINGS[J]. Acta Energiae Solaris Sinica. 2026, 47(8): 694-703 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0541

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