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改進(jìn)注意力機制的滾動(dòng)軸承故障診斷方法研究
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南京航空航天大學(xué)自動(dòng)化學(xué)院

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TH133.3;TG66

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Research on fault diagnosis method of rolling bearing with improved attention mechanism
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    摘要:

    針對滾動(dòng)軸承在實(shí)際工作環(huán)境中噪聲較大和負載變化的問(wèn)題,提出一種基于雙注意卷積機制的殘差神經(jīng)網(wǎng)絡(luò )(Double attention convolution mechanism ResNet,DACM_ResNet)軸承故障診斷方法:首先,對滾動(dòng)軸承振動(dòng)信號進(jìn)行短時(shí)傅里葉變換(short-time Fourier transform,STFT)并使用偽彩色處理得到三通道圖像數據;然后,對殘差神經(jīng)網(wǎng)絡(luò )在軸承故障診斷上進(jìn)行研究,在殘差單元的卷積層之后,使用DACM模塊,將殘差特征在通道和空間維度上進(jìn)行進(jìn)一步提取,最后,在凱斯西儲大學(xué)(CWRU)數據集上進(jìn)行試驗驗證,試驗結果表明所提出的方法在噪聲環(huán)境下及負載變化時(shí),平均診斷準確率達到了98%以上,說(shuō)明所提出的模型有較好的魯棒性。

    Abstract:

    Aiming the situation that rolling bearing in noisy and load changes at the actual working environment, A bearing fault diagnosis method based on Double attention convolution mechanism ResNet (DACM_ResNet) is proposed. First, short-time Fourier transform (STFT) is performed on the vibration signal of the rolling bearing and pseudo-color processing is used to obtain three-channel image data. Then, the residual neural network is studied on bearing fault diagnosis. After the convolution layer of the residual block, the DACM module is used to further extract the residual features from the two dimensions of channel and space, and the connection between the residual and the input is established. Finally, experiments were carried out on the Case Western Reserve University (CWRU) dataset, and the test results show that the proposed method has an average accuracy of more than 98% under the noise environment and load changes, indicating that the proposed model has good noise resistance

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肖安,李開(kāi)宇,范佳能,仲志強,賈銀亮.改進(jìn)注意力機制的滾動(dòng)軸承故障診斷方法研究計算機測量與控制[J].,2023,31(11):22-30.

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歷史
  • 收稿日期:2022-11-23
  • 最后修改日期:2023-02-20
  • 錄用日期:2023-02-20
  • 在線(xiàn)發(fā)布日期: 2023-11-23
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