郑州大学学报（理学版）2024，Vol.56Issue(2)：80-86,7.DOI:10.13705/j.issn.1671-6841.2022222
基于VMD和优化SSA-ELM的齿轮箱故障诊断
Gearbox Fault Diagnosis Based on VMD and Optimized SSA-ELM
摘要
Abstract
To address the problem of inadequate noise removal by traditional filters and low accuracy of model recognition,a gearbox fault diagnosis model of extreme learning machine(ELM)was proposed based on variational mode decomposition(VMD)and improved sparrow search algorithm(SSA).The gearbox signal was filtered out through improved selection of noisy components after VMD and wavelet packet threshold processing.Based on the extracted effective features in a time-frequency domain,SSA was improved by the Tent chaos mapping with the introduction of differential decrement factor,which op-timized the ELM for classification recognition.The experimental results showed that the classification ac-curacy of the proposed model for gearbox fault achieved 99.50%,and the convergence speed was faster while the fault diagnosis accuracy was improved,which verified the feasibility of the model.关键词
齿轮箱故障诊断/变分模态分解/小波包去噪/Tent混沌/麻雀搜索算法/极限学习机Key words
gearbox fault diagnosis/variational mode decomposition/wavelet packet denoising/Tent chaos/sparrow search algorithm/extreme learning machine分类
信息技术与安全科学引用本文复制引用
孟博,郇战,时文雅,余中舟,周靖诺,王佳晖..基于VMD和优化SSA-ELM的齿轮箱故障诊断[J].郑州大学学报（理学版）,2024,56(2):80-86,7.基金项目
国家自然科学基金项目(61772248). （61772248）
