Application of EMPE and KP-KELM in Fault Diagnosis of Planetary Gearbox

Bo Qin; Heng Yin; Zhuo Wang; Jianqiang Zhang; Zhijun Li; Jianguo Wang

doi:10.16578/j.issn.1004.2539.2019.05.028

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Application of EMPE and KP-KELM in Fault Diagnosis of Planetary Gearbox

Development·Application | 更新时间：2022-10-03

- Application of EMPE and KP-KELM in Fault Diagnosis of Planetary Gearbox
- Journal of Mechanical Transmission Vol. 43, Issue 5, Pages: 146-151(2019)
- 作者机构：
  
  1.内蒙古科技大学机械工程学院，内蒙古包头 014010
  2.内蒙古第一机械集团公司工艺研究所，内蒙古包头 014032
  3.特种车辆及其传动系统智能制造国家重点实验室，内蒙古包头 014032
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2019.05.028
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秦波,尹恒,王卓等.EMPE和KP-KELM在行星齿轮箱故障诊断中的应用[J].机械传动,2019,43(05):146-151.

Qin Bo,Yin Heng,Wang Zhuo,et al.Application of EMPE and KP-KELM in Fault Diagnosis of Planetary Gearbox[J].Journal of Mechanical Transmission,2019,43(05):146-151.
秦波,尹恒,王卓等.EMPE和KP-KELM在行星齿轮箱故障诊断中的应用[J].机械传动,2019,43(05):146-151. DOI： 10.16578/j.issn.1004.2539.2019.05.028.

Qin Bo,Yin Heng,Wang Zhuo,et al.Application of EMPE and KP-KELM in Fault Diagnosis of Planetary Gearbox[J].Journal of Mechanical Transmission,2019,43(05):146-151. DOI： 10.16578/j.issn.1004.2539.2019.05.028.

摘要

针对非线性、非平稳的行星齿轮箱振动信号故障特征“难提取”和基于核参数随机生成的高斯核极限学习机状态辨识模型分类精度低的问题，提出一种改进多尺度排列熵（Enhence Multi-scale Permutation Entropy，EMPE）与核极化高斯核极限学习机（Kernel Extreme Learning Machine，KELM）结合的行星齿轮箱状态辨识方法。首先，将经由形态平均滤波的行星齿轮箱行星齿轮的振动信号，借助于EMPE来获取多尺度下的排列熵值（Permutation Entropy，PE）构建高维特征向量集；其次，利用核极化（Kernel Polarization，KP）优化高斯核极限学习机的核参数,σ,；最后，将EMPE特征向量集作为输入，通过KP优化KELM算法的训练建立行星齿轮状态辨识模型。实验结果表明，与基于SVM和KELM的状态辨识模型相比，基于EMPE和KP-KELM的行星齿轮故障诊断方法具有更高的分类精度。

Abstract

According to the nonlinear and non-stationary of the planetary gearbox vibration signal fault characteristics is difficult to extract and the problem of low classification accuracy Gaussian kernel extreme learning machine based on random generation kernel parameters， a method for identifying the state of planetary gearboxes with enhence multi-scale permutation entropy （EMPE） and nuclear-polarized Gaussian kernel extreme learning machine （KELM） is proposed. Firstly， noise reduction of vibration signals of planetary gearbox planetary gears by morphological average filtering and using EMPE to obtain permutation entropy values at multiple scales to constructing eigenvector set. Secondly， kernel parameter ,σ, of Gaussian kernel extreme learning machine is optimized by kernel polarization. Finally， using the EMPE eigenvector set as input， the planetary gear state identification model is constructed by the training of KP-KELM algorithm. The experiment results show that， compared with the fault classification model based on SVM and KELM， the EMPE and KP-KELM planetary gear fault diagnosis method has higher classification accuracy and stronger generalization ability.

关键词

行星齿轮箱多尺度特征提取核参数优化状态辨识

Keywords

Planetary gearboxMulti resolution feature extractionKernel parameter optimizationState identification

references

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