期刊
MACHINES
卷 9, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/machines9120345
关键词
bearing fault diagnosis; deep learning; deep neural network
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)
The article introduces a novel method for bearing fault diagnosis using DNN, which improves feature extraction with multiple-domain image representation data and a multi-branch structure DNN, leading to better fault diagnosis performance.
Feature extraction from a signal is the most important step in signal-based fault diagnosis. Deep learning or deep neural network (DNN) is an effective method to extract features from signals. In this paper, a novel vibration signal-based bearing fault diagnosis method using DNN is proposed. First, the measured vibration signals are transformed into a new data form called multiple-domain image-representation. By this transformation, the task of signal-based fault diagnosis is transferred into the task of image classification. After that, a DNN with a multi-branch structure is proposed to handle the multiple-domain image representation data. The multi-branch structure of the proposed DNN helps to extract features in multiple domains simultaneously, and to lead to better feature extraction. Better feature extraction leads to a better performance of fault diagnosis. The effectiveness of the proposed method was verified via the experiments conducted with actual bearing fault signals and its comparisons with well-established published methods.
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