测控技术2026，Vol.45Issue(3)：36-43,8.DOI:10.19708/j.ckjs.2026.03.302
基于IPSO-RVM的行星齿轮箱故障识别方法
Fault Identification Method for Planetary Gearboxes Based on IPSO-RVM
李广元 1王燕山 1贾晨枫 1赵凯博1
作者信息
- 1. 北京长城航空测控技术研究所有限公司,北京 101111||自动化测试创新中心,北京 101111
- 折叠
摘要
Abstract
To address the issue that the standard particle swarm optimization(PSO)algorithm is prone to falling into local optima when optimizing the kernel parameters of the relevance vector machine(RVM),which leads to insufficient accuracy of the planetary gearbox fault diagnosis model,a new fault identification method based on improved particle swarm optimization and relevance vector machine(IPSO-RVM)is proposed.The PSO algo-rithm is deeply improved by introducing nonlinear decreasing inertia weight and asymmetric adaptive learning factors to systematically balance its global exploration and local exploitation capabilities.The improved IPSO algorithm is used to adaptively search for the optimal kernel function parameters of the RVM model,thereby es-tablishing an IPSO-RVM intelligent diagnosis model.The proposed method is applied to fault diagnosis experi-ments using measured vibration signals from a planetary gearbox.The results show that the average classifica-tion accuracy of the proposed model reaches 92.55％,which has an improvement of 4.91％and 10.44％com-pared to the traditional PSO-RVM and PSO-SVM models,respectively.The experiments demonstrate that the proposed method effectively overcomes the problem of the PSO algorithm falling into local optima,finds better parameters for the RVM model,and significantly enhances the generalization ability and robustness of the diag-nosis model,providing a solution with higher accuracy and efficiency for the intelligent fault diagnosis of plane-tary gearboxes.关键词
行星齿轮箱/故障诊断/改进粒子群优化/相关向量机/参数优化Key words
planetary gearbox/fault diagnosis/IPSO/RVM/parameter optimization分类
信息技术与安全科学引用本文复制引用
李广元,王燕山,贾晨枫,赵凯博..基于IPSO-RVM的行星齿轮箱故障识别方法[J].测控技术,2026,45(3):36-43,8.
