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基于改进GWO-LightGBM的滚动轴承未知故障诊断

柴琴琴 陈舒迪 王武 黄捷

柴琴琴, 陈舒迪, 王武, 黄捷. 基于改进GWO-LightGBM的滚动轴承未知故障诊断[J]. 航空动力学报, 2022, 37(4): 848-855. doi: 10.13224/j.cnki.jasp.20210221
引用本文: 柴琴琴, 陈舒迪, 王武, 黄捷. 基于改进GWO-LightGBM的滚动轴承未知故障诊断[J]. 航空动力学报, 2022, 37(4): 848-855. doi: 10.13224/j.cnki.jasp.20210221
CHAI Qinqin, CHEN Shudi, WANG Wu, HUANG Jie. Unknown fault diagnosis of rolling bearing based on improved GWO-LightGBM[J]. Journal of Aerospace Power, 2022, 37(4): 848-855. doi: 10.13224/j.cnki.jasp.20210221
Citation: CHAI Qinqin, CHEN Shudi, WANG Wu, HUANG Jie. Unknown fault diagnosis of rolling bearing based on improved GWO-LightGBM[J]. Journal of Aerospace Power, 2022, 37(4): 848-855. doi: 10.13224/j.cnki.jasp.20210221

基于改进GWO-LightGBM的滚动轴承未知故障诊断

doi: 10.13224/j.cnki.jasp.20210221
基金项目: 国家自然科学基金(61603094); 福州市科技计划项目(2019-G-44)
详细信息
    作者简介:

    柴琴琴(1985-),女,副教授,博士,主要从事故障诊断与模式识别研究。

  • 中图分类号: V263.6;TP206.3;TH133.3

Unknown fault diagnosis of rolling bearing based on improved GWO-LightGBM

  • 摘要: 针对滚动轴承未知新故障误判影响轴承安全性和检修效率的问题,提出了一种基于改进灰狼算法(GWO)和轻量级梯度提升机(LightGBM)的故障诊断模型,实现已知/未知故障的高精度判别。为避免单一尺度下特征提取的缺失,对滚动轴承振动信号分别提取时域、频域和小波域特征建立多域特征集。设计了带未知新故障判别机制的GWO-LightGBM模型,并构造含有Halton序列和模拟退火策略的GWO实现了模型参数有效优化。实例试验结果表明,模型对已知和未知类故障平均识别率达99.57%,10次随机试验平均识别率分别比单一分类模型逻辑回归(LR)、最近邻分类器(KNN)和支持向量机(SVM)高21.98%、17.00%、9.27%,验证了模型的有效性和优越性,能高准确率地识别出已知或以前从未出现的新故障。

     

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出版历程
  • 收稿日期:  2021-05-09
  • 刊出日期:  2022-04-28

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