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| A study on method of rolling bearing fault diagnosis based on Inception-BLSTM |
| ZHAO Kaihui1, WU Sicheng1, LI Tao2,3, HE Caichun4, ZHA Guotao4 |
1.College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China;
2.College of Traffic Engineering, Hunan University of Technology, Zhuzhou 412007, China;
3.CRRC Zhuzhou Institute Co., Ltd., Zhuzhou 412005, China; 4.Zhuzhou Times New Material Technology Co., Ltd., Zhuzhou 412007, China |
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Abstract To solve the defect of the conventional rolling bearing fault diagnosis method requiring a large amount of prior knowledge and easy to introduce error artificially, a method of rolling bearing fault diagnosis based on Inception-BLSTM was proposed, which combining the multiscale deep feature extraction ability of the Inception model and the advantage of bidirectional long short-term memory (BLSTM) neural network in sequence modeling.First, multiscale abstract features from the vibration signal of a rolling bearing were extracted using one-dimensional convolution kernels.Then the BLSTM was used for learning the time-dependence of features.Finally, the feature information was mapped to the corresponding fault mode through the full connection layer and the diagnosis result was obtained.The results show that the identification accuracy of the proposed method in multi-load scenarios is 996%, which has good load adaptability and anti-disturbance ability.
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Received: 18 January 2020
Published: 15 September 2021
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