Fault Diagnosis of Planetary Gearbox Key Component based ELMD Energy Entropy and AFSA-SVM

Zhang Luyang; Qin Bo; Yin Heng; Wang Jianguo

doi:10.16578/j.issn.1004.2539.2018.06.034

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Fault Diagnosis of Planetary Gearbox Key Component based ELMD Energy Entropy and AFSA-SVM

更新时间：2022-10-20

- Fault Diagnosis of Planetary Gearbox Key Component based ELMD Energy Entropy and AFSA-SVM
- Vol. 42, Issue 6, Pages: 164-170(2018)
- 作者机构：
  
  1. 内蒙古科技大学机械工程学院
  2. 内蒙古自治区机电系统智能诊断与控制重点实验室
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2018.06.034
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[1]张鲁洋,秦波,尹恒,王建国.基于ELMD能量熵与AFSA-SVM的行星齿轮箱关键部件故障诊断研究[J].机械传动,2018,42(06):164-170.

Zhang Luyang, Qin Bo, Yin Heng, et al. Fault Diagnosis of Planetary Gearbox Key Component based ELMD Energy Entropy and AFSA-SVM. [J]. 42(6):164-170(2018)
[1]张鲁洋,秦波,尹恒,王建国.基于ELMD能量熵与AFSA-SVM的行星齿轮箱关键部件故障诊断研究[J].机械传动,2018,42(06):164-170. DOI： 10.16578/j.issn.1004.2539.2018.06.034.

Zhang Luyang, Qin Bo, Yin Heng, et al. Fault Diagnosis of Planetary Gearbox Key Component based ELMD Energy Entropy and AFSA-SVM. [J]. 42(6):164-170(2018) DOI： 10.16578/j.issn.1004.2539.2018.06.034.

摘要

针对行星齿轮箱振动信号复杂时变调质特点使其"难表征",致使据此构建的状态辨识模型精度低的问题,提出一种基于总体局部均值分解（Ensemble local mean decomposition,ELMD）的能量熵与人工鱼群算法（Artificial fish swarm algorithm,AFSA）寻找支持向量机（Support vector machine,SVM）最优核函数系数组合的行星齿轮箱关键部件的状态辨识方法。首先,利用ELMD分解经形态平均滤波的行星齿轮箱关键部件的振动信号来获取若干窄带乘积函数（Product function,PF）。然后,计算其能量熵来构建高维特征向量集。最后,将其作为输入,通过训练学习建立AFSA优化SVM的行星齿轮箱关键部件状态辨识模型。实验结果表明,所提方法能凸显原信号中的有效故障成份,提高了模型的状态辨识精度。

Abstract

Aiming at the problem of complex modulation characteristics of vibration signals of the planetary gearbox make the state identification model low accuracy,a state identification method for key components of planetary gearbox based on combinations of ensemble local mean decomposition（ ELMD） energy entropy and artificial fish swarm algorithm finding support vector machine（ AFSA-SVM） optimal kernel function coefficient is proposed. To begin,a number of narrow-band product function（ PF） from vibration signals are obtained by ELMD with after morphological average filter. Then the high dimensional feature vector set is built by calculating the energy entropy of the above PF. At last,the fault diagnosis model is developed based on AFSA-SVM algorithm,in which the extracted fault features are employed as inputs. The experimental results show that the proposed method can show the fault component of the original signal with effectively. It has the state identification accurate of the model is greatly improved.

关键词

ELMD能量熵行星齿轮箱最优核函数系数AFSA-SVM

Keywords

ELMD energy entropyPlanetary gearboxOptimal kernel function coefficientAFSA-SVM

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