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基于变分模态分解和PSO-SVM的起重机齿轮箱故障诊断
引用本文:杨武帮,高丙朋,陈飞,张兴合,马伟栋. 基于变分模态分解和PSO-SVM的起重机齿轮箱故障诊断[J]. 机械传动, 2021, 45(4): 105-111. DOI: 10.16578/j.issn.1004.2539.2021.04.018
作者姓名:杨武帮  高丙朋  陈飞  张兴合  马伟栋
作者单位:新疆大学 电气工程学院,新疆 乌鲁木齐 830047;新疆维吾尔自治区特种设备检验研究院,新疆 乌鲁木齐 830011
基金项目:新疆维吾尔自治区自然科学
摘    要:起重机齿轮箱的振动信号具有信噪比低、非线性的特点,需要一定的专业知识和经验才能实现故障诊断.为了实现起重机齿轮箱的智能故障诊断,提出了一种基于变分模态分解(Variation?al modal decomposition,VMD)改进小波降噪和粒子群算法(Particle swarm optimization,PSO)...

关 键 词:起重机齿轮箱  变分模态分解  小波分解  粒子群算法  支持向量机

Fault Diagnosis of Crane Gearbox based on Variational Mode Decomposition and PSO-SVM
Yang Wubang,Gao Bingpeng,Chen Fei,Zhang Xinghe,Ma Weidong. Fault Diagnosis of Crane Gearbox based on Variational Mode Decomposition and PSO-SVM[J]. Journal of Mechanical Transmission, 2021, 45(4): 105-111. DOI: 10.16578/j.issn.1004.2539.2021.04.018
Authors:Yang Wubang  Gao Bingpeng  Chen Fei  Zhang Xinghe  Ma Weidong
Affiliation:(College of Electrical Engineering,Xinjiang University,Urumqi 830047,China;Institute of Special Equipment Inspection,Xinjiang Uygur Autonomous Region,Urumqi 830011,China)
Abstract:The vibration signal of crane gearbox has the characteristics of low signal-to-noise ratio and nonlinearity,so it needs some professional knowledge and experience to realize fault diagnosis.In order to real?ize intelligent fault diagnosis of crane gearbox,an intelligent fault diagnosis method based on variational modal decomposition(VMD)improved wavelet denoising and particle swarm optimization(PSO)support vector ma?chine(SVM)is proposed.Firstly,VMD is used to decompose the vibration signal to obtain the intrinsic mode function(IMF)of different scales.The decomposed high frequency component is improved after wavelet denoising and the low frequency component is reconstructed.Then the feature parameters of reconstructed signal are extracted to construct the feature vector,and kernel principal component analysis(KPCA)is used to real?ize the feature information fusion.Finally,the PSO optimized SVM is used for fault identification and classifica?tion.The experimental results show that the SVM model based on VMD improved wavelet signal preprocessing and PSO algorithm has high recognition accuracy and can effectively and accurately identify and classify the fault types of the crane gearbox.
Keywords:Crane gearbox  Variational mode decomposition  Wavelet decomposition  Particle swarm optimization  Support vector machine
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