Fault diagnosis of variable condition bearing based on improved dense network aided by class labels

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (17) : 204-212.

SUN Jiedi1,2, LIU Bao1, WEN Jiangtao3, SHI Peiming3, YAN Shengnan1,2, XIAO Qiyang4

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Abstract

Intelligent fault diagnosis-based data-driven for rolling bearings has been widely studied, however, most researches assume that training data and test data are the same distribution. The complex and variable working conditions in the actual operation of rotating machinery often lead to deviations in data distribution, which result in poor versatility and low identification accuracy. This paper introduced domain adaptation and proposed a transfer diagnosis network with synchronous adaptation of feature space domain and label probability distribution. The network combines a one-dimensional dense convolutional network and attention mechanism to automatically extract complex fault features; the domain adaptation constrains the network to learn domain invariant features by jointly minimizing the difference in feature probability distribution and label probability distribution; finally rolling bearing faults under variable operating conditions can be identified with high accuracy. The experimental results show the validity and better performance of this method.
Key words: bearing fault diagnosis; variable working conditions; dense convolution network; attention mechanism; label auxiliary

Key words

bearing fault diagnosis / variable working conditions / dense convolution network / attention mechanism / label auxiliary

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SUN Jiedi1,2, LIU Bao1, WEN Jiangtao3, SHI Peiming3, YAN Shengnan1,2, XIAO Qiyang4. Fault diagnosis of variable condition bearing based on improved dense network aided by class labels[J]. Journal of Vibration and Shock, 2022, 41(17): 204-212

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