Feature Extraction and Wear Damage Degree Identification of Planetary Gear based on PSO-FC Optimization KPCA

Yan He; Zongyan Wang

doi:10.16578/j.issn.1004.2539.2019.02.025

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Feature Extraction and Wear Damage Degree Identification of Planetary Gear based on PSO-FC Optimization KPCA

Test·Analysis | 更新时间：2022-10-09

- Feature Extraction and Wear Damage Degree Identification of Planetary Gear based on PSO-FC Optimization KPCA
- Journal of Mechanical Transmission Vol. 43, Issue 2, Pages: 137-143(2019)
- 作者机构：
  
  1.中北大学机械工程学院，山西太原 030051
  2.山西省起重机数字化设计工程技术研究中心，山西太原 030051
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2019.02.025
  CLC：
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贺妍,王宗彦.基于PSO-FC优化KPCA的特征提取及行星齿轮磨损损伤程度识别[J].机械传动,2019,43(02):137-143.

He Yan,Wang Zongyan.Feature Extraction and Wear Damage Degree Identification of Planetary Gear based on PSO-FC Optimization KPCA[J].Journal of Mechanical Transmission,2019,43(02):137-143.
贺妍,王宗彦.基于PSO-FC优化KPCA的特征提取及行星齿轮磨损损伤程度识别[J].机械传动,2019,43(02):137-143. DOI： 10.16578/j.issn.1004.2539.2019.02.025.

He Yan,Wang Zongyan.Feature Extraction and Wear Damage Degree Identification of Planetary Gear based on PSO-FC Optimization KPCA[J].Journal of Mechanical Transmission,2019,43(02):137-143. DOI： 10.16578/j.issn.1004.2539.2019.02.025.

摘要

行星齿轮传动系统发生故障时，其信号传递中相互耦合，呈现非线性的特性，使得行星齿轮的故障类型及损伤程度难以识别。借鉴模式识别中Fisher准则（FC）判别函数，构建核函数尺度参数优化的数学模型，应用改进的粒子群优化方法对其寻优，充分改善核主元分析法（KPCA）对于非线性问题的分析性能，将其应用于行星齿轮的磨损损伤程度的识别和诊断中。实例分析结果表明，基于PSO-FC智能优化后的KPCA改善了特征空间内数据分布结构，在行星齿轮的磨损损伤程度识别中取得了较好的尺度聚类效果，可以有效地解决复杂机械传动中损伤边界模糊、损伤程度难以识别的问题。

Abstract

When the planetary gear transmission system fails， its signal transmission is coupled with each other， and the nonlinear characteristic is presented， which makes the fault type and damage degree of the planetary gear to be difficultly recognized. The optimization mathematical model of the kernel function scale parameter is constructed by means of Fisher criterion（FC） discriminate function in pattern recognition， and the improved particle swarm optimization method is applied to the optimization to fully improve the analysis performance of the kernel principal component analysis（KPCA） for nonlinear problem. It is applied to the identification and diagnosis of wear damage degree of planetary gear. The results of example analysis show that the intelligently optimized KPCA based on PSO-FC have improve the structure distribution of data in the feature space and achieved the good scale clustering effect in planetary gear wear damage degree recognition. It can effectively solve the identify problems of complex fuzzy damage boundary and damage degree in the mechanical transmission.

关键词

粒子群优化核主元分析行星齿轮箱损伤程度识别Fisher准则

Keywords

Particle swarm optimization（PSO）Kernel principal component analysis （KPCA）Planetary gearboxDamage degree identificationFisher criterion

references

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魏秀业. 基于粒子群优化的齿轮箱故障诊断研究［D］. 太原：中北大学， 2009：5-6， 23， 27.

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