期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 100, 期 -, 页码 439-453出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2017.06.022
关键词
Intelligent fault diagnosis; Convolutional neural networks; Load domain adaptation; Anti-noise; End-to-end
资金
- National High-tech R&D Program of China (863 Program) [2015AA042201]
- National Natural Science Foundation of China [51275119]
- State Key Laboratory of Robotics and System (HIT) [SKLRS201708A]
In recent years, intelligent fault diagnosis algorithms using machine learning technique have achieved much success. However, due to the fact that in real world industrial applications, the working load is changing all the time and noise from the working environment is inevitable, degradation of the performance of intelligent fault diagnosis methods is very serious. In this paper, a new model based on deep learning is proposed to address the problem. Our contributions of include: First, we proposed an end-to-end method that takes raw temporal signals as inputs and thus doesn't need any time consuming denoising preprocessing. The model can achieve pretty high accuracy under noisy environment. Second, the model does not rely on any domain adaptation algorithm or require information of the target domain. It can achieve high accuracy when working load is changed. To understand the proposed model, we will visualize the learned features, and try to analyze the reasons behind the high performance of the model. (C) 2017 Published by Elsevier Ltd.
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