Fault Diagnosis of Train Bogie Bearing Based on Multi-scale Sample Entropy Improved Extreme Learning Machine
Train bogie bearing is affected by track excitation, uncertainty of running speed, impact vibration of track joints and vibration of other components. The vibration signal is nonlinear and non-stationary, which makes it difficult to extract fault characteristics and fault diagnosis accuracy is low....
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Control and Information Technology
2021-01-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.05.011 |
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| Summary: | Train bogie bearing is affected by track excitation, uncertainty of running speed, impact vibration of track joints and vibration of other components. The vibration signal is nonlinear and non-stationary, which makes it difficult to extract fault characteristics and fault diagnosis accuracy is low. In view of above problems, a fault diagnosis method of train bogie bearing based on multi-scale sample entropy improved extreme learning machine is proposed. Firstly, fault features are extracted by using the multi-scale sample entropy to form a feature vector set. Then, the extreme learning machine is optimized by particle swarm optimization, and the input weights and hidden node thresholds are obtained. Finally, the feature vector set is divided into test set and training set, and the improved extreme learning machine is used as a pattern recognition algorithm for fault pattern recognition. Experimental results show that the proposed method can effectively carry out fault pattern recognition, and the recognition accuracy reaches 96 %, which is suitable for train bearing fault diagnosis. |
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| ISSN: | 2096-5427 |