Improved Deep Learning Fusion Model in Fault Diagnosis

被引：0
|
作者：
Wang Y. [1 ]
Duan X. [2 ]
机构：
[1] Department of Electronic and Optical Engineering, Army Engineering University of PLA, Shijiazhuang
[2] School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang
来源：
Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis | 2019年 / 39卷 / 06期
关键词：
Adaptive learning rate; Deep belief network; Denoising autoencoder; Fault diagnosis; Robustness; Support vector machine;
D O I：
10.16450/j.cnki.issn.1004-6801.2019.06.019
中图分类号：
学科分类号：
摘要：
In light of the problems caused by complex equipment structure, working environment noise, and big data, a deep learning fusion model is proposed to achieve efficient and accurate fault diagnosis. First, the denoising autoencoder is used to process the random noise of the original signal and learn the low-level features. Then, the deep belief network is used to learn the deep features based on the learned low-level features. Finally, the fused depth features are fed into the PSO-SVM to train the intelligent diagnosis model. The proposed method is applied to the fault diagnosis of rolling bearings, The results show that the method proposed is more efficient and robust than the existing methods. © 2019, Editorial Department of JVMD. All right reserved.
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页码：1271 / 1276
页数：5
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