[1] Ding Yunhao, Jia Minping,. A multi-scale convolutional auto-encoder and its applicationin fault diagnosis of rolling bearings [J]. Journal of Southeast University (English Edition), 2019, 35 (4): 417-423. [doi:10.3969/j.issn.1003-7985.2019.04.003]

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A multi-scale convolutional auto-encoder and its applicationin fault diagnosis of rolling bearings()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

35

Issue:

2019 4

Page:

417-423

Research Field:

Mechanical Engineering

Publishing date:

2019-12-30

Info

Title:

A multi-scale convolutional auto-encoder and its applicationin fault diagnosis of rolling bearings

Author(s):

Ding Yunhao;  Jia Minping

School of Mechanical Engineering, Southeast University, Nanjing 211189, China

Keywords:

fault diagnosis;  deep learning;  convolutional auto-encoder;  multi-scale convolutional kernel;  feature extraction

PACS:

TH133.3;TP18

DOI:

10.3969/j.issn.1003-7985.2019.04.003

Abstract:

Aiming at the difficulty of fault identification caused by manual extraction of fault features of rotating machinery, a one-dimensional multi-scale convolutional auto-encoder fault diagnosis model is proposed, based on the standard convolutional auto-encoder. In this model, the parallel convolutional and deconvolutional kernels of different scales are used to extract the features from the input signal and reconstruct the input signal; then the feature map extracted by multi-scale convolutional kernels is used as the input of the classifier; and finally the parameters of the whole model are fine-tuned using labeled data. Experiments on one set of simulation fault data and two sets of rolling bearing fault data are conducted to validate the proposed method. The results show that the model can achieve 99.75%, 99.3% and 100% diagnostic accuracy, respectively. In addition, the diagnostic accuracy and reconstruction error of the one-dimensional multi-scale convolutional auto-encoder are compared with traditional machine learning, convolutional neural networks and a traditional convolutional auto-encoder. The final results show that the proposed model has a better recognition effect for rolling bearing fault data.

References:

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Memo

Memo:

Biographies: Ding Yunhao(1992—), male, graduate; Jia Minping(corresponding author), male, doctor, professor, mpjia@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No.51675098).
Citation: Ding Yunhao, Jia Minping. A multi-scale convolutional auto-encoder and its application in fault diagnosis of rolling bearings[J].Journal of Southeast University(English Edition), 2019, 35(4):417-423.DOI:10.3969/j.issn.1003-7985.2019.04.003.

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