Gear Fault Diagnosis Method based on Multi-domain Feature and Improved D-S Evidence Theory

Fang Liu; Yanxue Wang

doi:10.16578/j.issn.1004.2539.2019.09.028

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Gear Fault Diagnosis Method based on Multi-domain Feature and Improved D-S Evidence Theory

Development·Application | 更新时间：2022-09-24

- Gear Fault Diagnosis Method based on Multi-domain Feature and Improved D-S Evidence Theory
- Journal of Mechanical Transmission Vol. 43, Issue 9, Pages: 159-165(2019)
- 作者机构：
  
  1.桂林电子科技大学机电工程学院，广西桂林 541004
  2.北京建筑大学城市轨道交通车辆服役性能保障北京市重点实验室，北京 100044
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2019.09.028
  CLC：
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刘放,王衍学.基于多域特征与改进D-S证据理论的齿轮故障智能诊断方法[J].机械传动,2019,43(09):159-165.

Liu Fang,Wang Yanxue.Gear Fault Diagnosis Method based on Multi-domain Feature and Improved D-S Evidence Theory[J].Journal of Mechanical Transmission,2019,43(09):159-165.
刘放,王衍学.基于多域特征与改进D-S证据理论的齿轮故障智能诊断方法[J].机械传动,2019,43(09):159-165. DOI： 10.16578/j.issn.1004.2539.2019.09.028.

Liu Fang,Wang Yanxue.Gear Fault Diagnosis Method based on Multi-domain Feature and Improved D-S Evidence Theory[J].Journal of Mechanical Transmission,2019,43(09):159-165. DOI： 10.16578/j.issn.1004.2539.2019.09.028.

摘要

为了能全面准确识别齿轮的故障类别，建立了基于时域、频域以及能量等多域特征参数的特征空间模型。在此基础上，提出了一种基于多域特征与改进D-S证据理论的齿轮故障智能诊断方法。通过实验台实测数据提取相关特征参数作为诊断样本，以粒子群优化支持向量机的初步诊断结果构建多个证据体。实验结果验证了改进D-S证据理论融合证据体诊断结果的有效性。

Abstract

In order to fully and accurately identify the fault category of gear，a feature space model based on multi-domain characteristic parameters such as time domain，frequency domain and energy is established. On this basis, an intelligent fault diagnosis method based on multi-domain feature and improved D-S evidence theory is proposed. Relevant feature parameters are extracted from the measured data as the diagnostic samples，and multiple evidences are constructed with the preliminary diagnosis results of particle swarm optimization support vector machine（PSO-SVM）. The experimental results verify the effectiveness of the final diagnosis results obtained by the improved D-S evidence theory in this work.

关键词

齿轮故障诊断粒子群优化支持向量机加权D-S证据理论信息融合

Keywords

Gear fault diagnosisPSO-SVMWeighted D-S evidence theoryInformation fusion

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