MOTHED FOR AUTOMATED IDENTIFICATION OF ABNORMAL CONDITIONS IN UNSUPERVISED WIND TURBINE GEARBOXES

Li Jialin; Liu Yuxin; Cao Xuan; Bai Houyi; Chen Renxiang

doi:10.19912/j.0254-0096.tynxb.2025-0575

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (8) : 715-723. DOI: 10.19912/j.0254-0096.tynxb.2025-0575

MOTHED FOR AUTOMATED IDENTIFICATION OF ABNORMAL CONDITIONS IN UNSUPERVISED WIND TURBINE GEARBOXES

Li Jialin^1,2, Liu Yuxin¹, Cao Xuan¹, Bai Houyi², Chen Renxiang¹

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Abstract

Significant progress has been made in the fault diagnosis of wind turbine planetary gearboxes based on deep learning. To address the difficulty to obtain labelled data for training classification models in the task of identifying abnormal states of planetary gearboxes, this paper proposes an unsupervised automatic detection method, in which the Log Mel-band Energies features of the original vibration signals are firstly extracted and input as training data into an unsupervised abnormal state recognition model based on U-net autoencoder as the core for training. Furthermore, based on the reconstruction error between the model inputs and the outputs, a judgment threshold for the health state of the gearbox is set to complete the abnormal state identification task. In this paper, both factory test data of gearboxes and actual operation data of wind turbines from a wind farm in Yangtouya, Shanxi Province are used to validate the reliability of the proposed model. For factory gearboxes, a signal processing method based on spectral amplitude modulation is simultaneously adopted for dual verification. The proposed method achieved an identification accuracy of 93.34% on the factory dataset and the wind farm dataset. This proves that the proposed unsupervised method can automatically and correctly separate abnormal state wind turbine gearboxes.

Key words

unsupervised learning / wind turbines / gearboxes / anomaly detection / Log Mel-band energies / autoencoder / spectral amplitude modulation

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Li Jialin, Liu Yuxin, Cao Xuan, Bai Houyi, Chen Renxiang. MOTHED FOR AUTOMATED IDENTIFICATION OF ABNORMAL CONDITIONS IN UNSUPERVISED WIND TURBINE GEARBOXES[J]. Acta Energiae Solaris Sinica. 2026, 47(8): 715-723 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0575

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