PSO稀疏分解在齿轮信号故障特征提取中的应用  

PSO Sparse Decomposition and Its Application in the Fault Signal Feature Extraction of Gear

作　　者：巩孟林 陈卫[1] 钟也磐 GONG Menglin;CHEN Wei;ZHONG Yepan(Aeronautics Engineering College,Air Force Engineering Universityrsity,Xi'an 710038,China)

机构地区：[1]空军工程大学航空工程学院,西安710038

出　　处：《电子工程学院学报》2019年第3期47-52,共6页Journal of The College of Electronic Engineering

基　　金：国家自然科学基金(51175509).

摘　　要：针对齿轮早期故障诊断,传统的信号处理方法受噪声干扰大,严重影响了齿轮故障特征提取。结合粒子群(PSO)算法和稀疏分解算法提出PSO稀疏分解,利用PSO在搜索最优解方面的优势降低了稀疏分解的计算复杂度,并提出了"匹配度"作为信号的特征量。通过对模拟信号和某型航空发动机齿轮毂振动信号的分析,证明PSO稀疏分解在强噪声背景下具有很好的稳健性,提高了振动信号的信噪比,能够有效提取齿轮的故障特征,故障信号的"匹配度"比正常信号平均高出0.4左右,与传统方法相比,优势较为明显。As for the fault diagnosis of gear at early stage,the conventional methods of signal processing are significantly interfered by noise,blocking the fault feature extraction of gear.This paper proposes a PSO sparse decomposition combined with PSO(Particle swarm optimization)algorithm and sparse decomposition algorithm,lowering the computing complexity of sparse decomposition,and also proposes a‘Matching index’as the signal feature.The research result of the simulated signal indicates that PSO decomposition performs well under condition of strong noise and improves the SNR greatly.What’s more,the PSO sparse decomposition is proved efficiently in fault signal feature extraction of gear through the analysis of the signal from aero-engine gear hub.The‘Matching index’of fault signal is 0.4 higher equally than that of normal signal.This is superior obviously to the traditional methods.

关 键 词：齿轮故障诊断 稀疏分解 粒子群算法 MORLET小波 

分 类 号：V2328[航空宇航科学与技术—航空宇航推进理论与工程] TH13Z46[机械工程—机械制造及自动化]