Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM

Zhao Xiaohui; Tan Qi; Hu Sheng; Yang Wenbin; Huan Kaixuan; Zhang Zhijie

doi:10.16578/j.issn.1004.2539.2023.02.021

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Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM

Development·Application | 更新时间：2023-02-25

- Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM
- Journal of Mechanical Transmission Vol. 47, Issue 2, Pages: 157-163(2023)
- 作者机构：
  
  西安工程大学机电工程学院，陕西西安 710048
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2023.02.021
  CLC：
- Published：15 February 2023，
  
  Received：07 June 2022，
  
  Revised：27 June 2022，
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赵小惠,谭琦,胡胜等.基于LMD云模型与PSO-KELM的齿轮箱故障诊断[J].机械传动,2023,47(02):157-163.

Zhao Xiaohui,Tan Qi,Hu Sheng,et al.Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM[J].Journal of Mechanical Transmission,2023,47(02):157-163.
赵小惠,谭琦,胡胜等.基于LMD云模型与PSO-KELM的齿轮箱故障诊断[J].机械传动,2023,47(02):157-163. DOI： 10.16578/j.issn.1004.2539.2023.02.021.

Zhao Xiaohui,Tan Qi,Hu Sheng,et al.Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM[J].Journal of Mechanical Transmission,2023,47(02):157-163. DOI： 10.16578/j.issn.1004.2539.2023.02.021.

摘要

由于齿轮箱故障振动信号具有非平稳性与不确定性的特点，导致齿轮箱故障诊断精度较低。针对该问题提出一种基于局部均值分解（LMD）云模型特征提取结合粒子群优化（PSO）核极限学习机（KELM）的齿轮箱故障诊断方法。首先，将故障振动信号用LMD分解得到若干PF分量，并利用相关系数原则筛选出相关性较高的PF分量；其次，在云模型中输入筛选后的PF分量，采用逆向云发生器对特征向量进行提取并输入到PSO-KELM中进行故障诊断；最后，利用QPZZ-Ⅱ实验台齿轮箱实测数据对该方法进行了性能分析。结果表明，该方法识别精度为97.65%，与多种方法进行对比，该方法具备最佳识别性能。

Abstract

The characteristics of non-smoothness and uncertainty of gearbox fault vibration signal lead to the low accuracy of gearbox fault diagnosis. To address this problem

a gearbox fault diagnosis method based on local mean decomposition (LMD) cloud model feature extraction combined with particle swarm optimization (PSO) kernel extreme learning machine (KELM) is proposed. Firstly

the fault vibration signal is decomposed by LMD to obtain several PF components

and the PF components with higher correlation are screened out using the correlation coefficient principle. Secondly

the screened PF components are input into the cloud model

and the feature vectors are extracted using the inverse cloud generator and input into PSO-KELM for fault diagnosis. Finally

the performance of the method is analyzed using the measured data of the QPZZ-Ⅱ test-bed gearbox. The results show that the recognition accuracy of the method is 97.65%

and compared with various methods this method has the best recognition performance.

关键词

齿轮箱故障诊断局部均值分解云模型粒子群优化核极限学习机

Keywords

GearboxFault diagnosisLocal mean decompositionCloud modelParticle swarm optimization kernel extreme learning machine

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