Abstract
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.
Original language | English |
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Article number | e345 |
Number of pages | 15 |
Journal | Machines |
Volume | 9 |
Issue number | 12 |
Early online date | 09 Dec 2021 |
DOIs | |
Publication status | Early online date - 09 Dec 2021 |
Keywords
- bearing fault diagnosis
- deep learning
- deep neural network