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A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis
| Content Provider | MDPI |
|---|---|
| Author | Hoang, Duy Tang Tran, Xuan Toa Van, Mien Kang, Hee Jun |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 244 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21010244 |
| Journal | Sensors |
| Issue Number | 1 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-01-01 |
| Access Restriction | Open |
| Subject Keyword | Sensors Industrial Engineering Bearing Fault Diagnosis Deep Learning Deep Neural Network Sensor Fusion |
| Content Type | Text |
| Resource Type | Article |
