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Fault Diagnosis of Motor Vibration Signals by Fusion of Spatiotemporal Features
| Content Provider | MDPI |
|---|---|
| Author | Wang, Lijing Zhang, Chunda Zhu, Juan Xu, Fengxia |
| Copyright Year | 2022 |
| Description | This paper constructs a spatiotemporal feature fusion network (STNet) to enhance the influence of spatiotemporal features of signals on the diagnostic performance during motor fault diagnosis. The STNet consists of the spatial feature processing capability of convolutional neural networks (CNN) and the temporal feature processing capability of recurrent neural networks (RNN). It is used for fault diagnosis of motor vibration signals. The network uses dual-stream branching to extract the fault features of motor vibration signals by a convolutional neural network and gated recurrent unit (GRU) simultaneously. The features are also enhanced by using the attention mechanism. Then, the temporal and spatial features are fused and input into the softmax function for fault discrimination. After that, the fault diagnosis of motor vibration signals is completed. In addition, several sets of experimental evaluations are conducted. The experimental results show that the vibration signal processing method combined with spatiotemporal features can effectively improve the recognition accuracy of motor faults. |
| Starting Page | 246 |
| e-ISSN | 20751702 |
| DOI | 10.3390/machines10040246 |
| Journal | Machines |
| Issue Number | 4 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-03-30 |
| Access Restriction | Open |
| Subject Keyword | Machines Industrial Engineering Spatiotemporal Feature Fusion Convolutional Neural Network Gated Recurrent Unit Attention Mechanism Fault Diagnosis |
| Content Type | Text |
| Resource Type | Article |
