A fault diagnosis model of rotating machinery based on KD-DenseNet

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (16) : 39-45.

WANG Taiyong，GONG Liming，WANG Peng，QIAO Huihui，REN Dong

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Abstract

The traditional fault diagnosis algorithm lacks good adaptability and generalization due to the complex and changeable working conditions of rotating machinery. To solve the problems, an intelligent fault diagnosis model KD-DenseNet based on kernel dropout (KD) is proposed. Firstly, preprocessing the original vibration signals of various fault states by overlapping and segmenting. Then, the pre-processed data are trained as input of KD-DenseNet, at the same time, dropout is applied to kernel to improve the processing speed and anti-interference ability of the model for vibration signals. Finally, the result of fault type determination is obtained. The application of KD-DenseNet avoids the gradient dispersion phenomenon, improves the extraction efficiency of effective features, and solves the problems that traditional feature extraction methods can not mine features and adapt to task adjustment effectively.

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fault diagnosis / rolling bearing / variable conditions / deep learning / kernel dropout

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WANG Taiyong，GONG Liming，WANG Peng，QIAO Huihui，REN Dong. A fault diagnosis model of rotating machinery based on KD-DenseNet[J]. Journal of Vibration and Shock, 2020, 39(16): 39-45

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