Deep transfer fault diagnosis method for extra-large bearings based on GADF-MDSC

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (19) : 10-18.

JIANG Yefei1, WANG Hua1, PAN Yubin1, WANG Tianxiang1, FU Hang2

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Abstract

Aiming at the problem of incomplete fault feature extraction due to complex operating conditions and lack of fault data of oversized bearing in engineering applications, an oversized bearing intelligent diagnosis method of deep migration based on Gramian angular difference field and multi-scale depthwise separable convolutions (GADF-MDSC) was proposed. Firstly, the GADF-MDSC fault diagnosis network is constructed, which is divided into three modules: image conversion, feature extraction and output. In the image conversion module, the vibration signal is converted into two dimensional image by using the GADF. The feature extraction module extracts comprehensive fault feature information by MDSC, and filters fusion features by bidirectional gated cycle unit. The output part predicts the probability distribution of bearing failure types by Softmax function. Then, the source domain data is used to pre-train the model, the weight parameters of the pre-trained model are used as the initial parameters of the target domain training model, all parameters except the bottom layer are frozen, and the target domain data is used to fine-tune the model to achieve the deep migration fault diagnosis task. Finally, the deep migration model is verified by two kinds of oversized bearing tests. The experimental results show that the proposed method can still guarantee high cross-working accuracy of 86.04% when the target domain sample is only 5%, and the migration effect is better than other methods.

Key words

oversized bearing / Fault diagnosis / Transfer learning / Gramian Angular Field / Multi-scale convolution / Depthwise separable convolution

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JIANG Yefei1, WANG Hua1, PAN Yubin1, WANG Tianxiang1, FU Hang2. Deep transfer fault diagnosis method for extra-large bearings based on GADF-MDSC[J]. Journal of Vibration and Shock, 2024, 43(19): 10-18

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