期刊
SENSORS
卷 21, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/s21010244
关键词
bearing fault diagnosis; deep learning; deep neural network; sensor fusion
资金
- Basic Science Research Program through the National Research Foundation of Korea(NRF) - Ministry of Education [2019R1D1A3A03103528]
- National Research Foundation of Korea [2019R1D1A3A03103528] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper introduces a novel method for bearing fault diagnosis by fusing information from multiple sensor systems using a convolutional neural network. Experimental results show that the proposed method outperforms other techniques in feature extraction and fusion processes.
This paper presents a novel method for fusing information from multiple sensor systems for bearing fault diagnosis. In the proposed method, a convolutional neural network is exploited to handle multiple signal sources simultaneously. The most important finding of this paper is that a deep neural network with wide structure can extract automatically and efficiently discriminant features from multiple sensor signals simultaneously. The feature fusion process is integrated into the deep neural network as a layer of that network. Compared to single sensor cases and other fusion techniques, the proposed method achieves superior performance in experiments with actual bearing data.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据