FAULT DIAGNOSIS OF WIND TURBINE GEARBOXES BASED ON GGD-EFFICIENTNET AND VOICEPRINT RECOGNITION

Liao Lida; Chen Weike; Luo Xiao; Shu Wangyong; Zhang Zhiming; Dai Jun

doi:10.19912/j.0254-0096.tynxb.2023-2171

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (4) : 570-578. DOI: 10.19912/j.0254-0096.tynxb.2023-2171

FAULT DIAGNOSIS OF WIND TURBINE GEARBOXES BASED ON GGD-EFFICIENTNET AND VOICEPRINT RECOGNITION

Liao Lida, Chen Weike, Luo Xiao, Shu Wangyong, Zhang Zhiming, Dai Jun

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Abstract

This study presents a novel approach for diagnosing gearbox faults based on noise generated by faulty gears, utilizing a high-performance neural network called Grouped GCNet and Depthwise Separable Convolution with EfficientNet （GGD-EfficientNet）. Noise signals from faulty gears were experimentally acquired and categorized into six distinct types according to their operational states. the Log-Mel spectral method was employed to extract the spectrograms of these noise signals. Recognizing the limitations of EfficientNet in feature extraction from gearbox fault spectrograms, we enhanced its performance by integrating the Group-based Enhanced Global Context Network （GE-GCNet） and Depthwise Separable Convolution （DSCConv）. Experimental results demonstrate that the proposed GGD-EfficientNet achieves an impressive accuracy of 99.7% on the spectrogram dataset of faulty gears, effectively classifying fault types and providing valuable insights for gearbox fault diagnosis.

Key words

wind turbines / gears / fault detection / GGD-EfficientNet / voiceprint recognition

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Liao Lida, Chen Weike, Luo Xiao, Shu Wangyong, Zhang Zhiming, Dai Jun. FAULT DIAGNOSIS OF WIND TURBINE GEARBOXES BASED ON GGD-EFFICIENTNET AND VOICEPRINT RECOGNITION[J]. Acta Energiae Solaris Sinica. 2025, 46(4): 570-578 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2171

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