基于Inception-BLSTM的滚动轴承故障诊断方法研究

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振动与冲击 ›› 2021, Vol. 40 ›› Issue (17) : 290-297.

论文

赵凯辉1，吴思成1，李涛2,3，贺才春4，查国涛4

作者信息 +

A study on method of rolling bearing fault diagnosis based on Inception-BLSTM

ZHAO Kaihui1, WU Sicheng1, LI Tao2,3, HE Caichun4, ZHA Guotao4

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文章历史 +

摘要

针对传统的滚动轴承故障诊断方法依赖大量先验知识以及容易人为引入误差等缺点，结合Inception模型的多尺度抽象特征提取能力与双向长短时记忆(BLSTM)神经网络序列建模的优势，提出一种基于Inception-BLSTM的滚动轴承故障诊断方法。首先，设计Inception模型从滚动轴承振动信号中提取出多尺度抽象特征。其次，设计BLSTM进一步学习特征信息的时间依赖性。最后，通过全连接层将特征信息映射到对应的故障模式并得出诊断结果。实验结果表明，该方法在多负载场景下的轴承故障识别精度达到了996%，具有良好的负载适应性以及抗干扰能力。

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 996%, which has good load adaptability and anti-disturbance ability.

导出引用

赵凯辉，吴思成，李涛，贺才春，查国涛. 基于Inception-BLSTM的滚动轴承故障诊断方法研究[J]. 振动与冲击, 2021, 40(17): 290-297

ZHAO Kaihui, WU Sicheng, LI Tao HE Caichun, ZHA Guotao. A study on method of rolling bearing fault diagnosis based on Inception-BLSTM[J]. Journal of Vibration and Shock, 2021, 40(17): 290-297

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