FAULT DIAGNOSIS METHOD OF WIND TURBINE BEARING BASED ON AM AND CNN MULTI-LEVEL FEATURE FUSION

Wang Jinhua; Han Jinyu; Cao Jie; Wang Yali

doi:10.19912/j.0254-0096.tynxb.2023-0081

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (5) : 51-61. DOI: 10.19912/j.0254-0096.tynxb.2023-0081

FAULT DIAGNOSIS METHOD OF WIND TURBINE BEARING BASED ON AM AND CNN MULTI-LEVEL FEATURE FUSION

Wang Jinhua^1-3, Han Jinyu¹, Cao Jie^1,4, Wang Yali¹

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Abstract

In this paper, we proposed a attention mechanism multi-level feature fusion convolutional neural network（A2ML2F-CNN） fault diagnosis method. The method takes the original current and vibration signals as inputs, firstly uses the attention mechanism convolutional neural network （AMCNN） module to extract the data signal features separately, and perform a first-level feature fusion connection. On this basis, the attention mechanism one-dimensional convolutional neural network （AM1DCNN） and the two-dimensional convolutional neural network （2DCNN） are used to extract relevant information, and perform a secondary feature fusion, to solves the problem of insufficient fault information of single-sensor data and the difficulty of extracting complementary features. Finally, the fully connected layer and the Softmax layer are used to classify and the diagnostic results are obtained. In order to verify the fault diagnosis effect of the method proposed in this paper, the Paderborn data set is used for experimental verification, and its diagnosis effect is compared with CNN, LSTM, SVM. The results showed that the diagnostic accuracy of the method in this paper increased by 1.8, 3.2 and 4.8 percentage points respectively compared to the above methods, which shows the effectiveness of the method in this paper.

Key words

wind turbines / fault diagnosis / feature fusion / attention mechanism / convolutional neural network（CNN） / wind turbine bearings

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Wang Jinhua, Han Jinyu, Cao Jie, Wang Yali. FAULT DIAGNOSIS METHOD OF WIND TURBINE BEARING BASED ON AM AND CNN MULTI-LEVEL FEATURE FUSION[J]. Acta Energiae Solaris Sinica. 2024, 45(5): 51-61 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0081

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