基于SSA-VMD和1.5维包络谱的齿轮箱磨损故障诊断的研究  

Research on fault diagnosis of gearbox wear based on SSA-VMD and 1.5-dimensional envelope spectrum

作　　者：毕浩程 蒋章雷[1] 吴国新[1] 刘秀丽[1] 栾忠权[1] BI Haocheng;JIANG Zhanglei;WU Guoxin;LIU Xiuli;LUAN Zhongquan(The Ministry of Education Key Laboratory of Modern Measurement and Control Technology,Beijing Information Science&Technology University,Beijing 100192,China)

机构地区：[1]北京信息科技大学现代测控技术教育部重点实验室,北京100192

出　　处：《现代制造工程》2022年第5期130-137,23,共9页Modern Manufacturing Engineering

基　　金：国家重点研发计划项目(2020YFB1713203);国家自然科学基金面上项目(61973041,51975058);北京市教委科研计划项目(KM202011232001);北京市教委科技计划一般项目(KM201811232023);北京学者计划项目(2015-025)。

摘　　要：针对强噪声背景下行星齿轮箱特征提取困难的问题,提出一种基于樽海鞘群算法优化变分模态分解(SSA-VMD)结合1.5维包络谱的故障诊断方法。该方法首先运用樽海鞘群算法(SSA)优化变分模态分解(VMD)的参数;然后运用自相关系数对分解信号进行重构,降低噪声的干扰;最后运用1.5维包络谱对重构信号进行故障的特征提取。在实验部分,首先通过仿真试验将SSA-VMD与变分模态分解(VMD)进行对比,验证了SSA-VMD的优越性;然后搭建行星齿轮箱磨损故障全生命周期实验台采集振动信号,运用SSA-VMD结合1.5维包络谱的方法提取出了振动信号的故障特征频率,总结了行星齿轮箱磨损故障演化规律。研究结果表明:随着磨损故障程度的加深,故障特征频率出现次数明显增多,凭借这一规律,有利于实现对行星齿轮箱磨损故障的诊断;该结果可为行星齿轮箱磨损的故障诊断提供依据。Aiming at the difficulty of feature extraction of planetary gearboxes under the background of strong noise, a fault diagnosis method based on Salvia Squirt Group Algorithm optimized Variational Mode Decomposition(SSA-VMD) combined with 1.5-dimensional envelope spectrum was proposed.First, SSA was used to optimize the parameters of VMD;then the decomposed signal was reconstructed to reduce noise interference by the autocorrelation coefficient;and finally the diagnosis characteristics of the reconstructed signal were extracted by the 1.5-dimensional envelope spectrum.In the experimental part, SSA-VMD first was used to compare with VMD through simulation experiments to verify the superiority of SSA-VMD;then a planetary gearbox wear failure full life cycle test bench was set up to collect vibration signals, and the fault characleristic frequency of vibration signal was extracted by SSA-VMD combined with the 1.5-dimensional envelope spectrum method, and the evolution law of wear failure of planetary gearbox was summarized.The research results indicate that with the deepening of the degree of wear failure, the number of times of wear failure characteristic frequency increase significantly;relying on this rule, it is helpful to realize the diagnosis of planetary gearbox wear failure. This result can provide a favorable basis for the diagnosis of planetary gearbox wear failure.

关 键 词：变分模态分解 樽海鞘群算法 1.5维包络谱 行星齿轮箱 故障诊断 

分 类 号：TH132.4[机械工程—机械制造及自动化]