数据驱动的凸轮式绝对重力仪微小故障诊断  

Data-Driven Incipient Fault Diagnosis for Cam-Driven Absolute Gravimeter

作　　者：牟宗磊 王晨 张媛[2] 郝妮妮[2] 胡若 MOU Zonglei;WANG Chen;ZHANG Yuan;HAO Nini;HU Ruo(College of Electrical Engineering and Automation,Shandong University of Science and Technology Qingdao,266590,China;College of Mechanical and Electronic Engineering,Shandong University of Science and Technology Qingdao,266590,China;National Institute of Metrology Beijing,100029,China)

机构地区：[1]山东科技大学电气与自动化工程学院,青岛266590 [2]山东科技大学机械电子工程学院,青岛266590 [3]中国计量科学研究院,北京100029

出　　处：《振动．测试与诊断》2022年第6期1068-1075,1240,共9页Journal of Vibration,Measurement & Diagnosis

基　　金：国家重点研发计划资助项目(2022YFF0607504);山东省自然科学基金资助项目(ZR2020KE061,ZR2021MF027)。

摘　　要：针对凸轮式绝对重力仪微小故障幅值小、故障特征微弱及易被噪声掩盖而难于发现等特点,提出了一种融合改进集总平均经验模态分解(modified ensemble empirical mode decomposition,简称MEEMD)、能量熵以及多尺度排列熵(multi-scale permutation entropy,简称MPE)的凸轮式绝对重力仪微小故障诊断方法。通过MEEMD算法对凸轮式绝对重力仪不同工况下的振动信号进行自适应分解,筛选出有效的本征模态函数(intrinsic mode function,简称IMF),提取出振动数据中具有敏感特征的多尺度排列熵和能量熵,将提取的多维特征向量矩阵输入到以径向基函数(radial basis function,简称RBF)为核函数的支持向量机(support vector machine,简称SVM)中,基于数据实现了凸轮式绝对重力仪微小故障的精确诊断。试验结果表明,该方法可以有效区分凸轮式绝对重力仪的各类微小故障,识别准确度达到97.1%,解决了因微小故障导致凸轮式绝对重力仪测量精度低的问题,实现了重力仪微小故障的快速溯源和精准定位,具有较好的工程应用前景。The vibration caused by incipient fault is the main factor affecting the accuracy of cam-driven absolute gravimeter.An incipient fault diagnosis method for cam-driven absolute gravimeter is proposed with the modi⁃fied ensemble empirical mode decomposition(MEEMD),multi-scale permutation entropy(MPE)and energy entropy,based on the incipient fault features of smaller amplitude,weak fault characteristics and hard-to-find in the noise.After the adaptive decomposition of the vibration signal of the cam-driven absolute gravimeter under different working conditions,the effective intrinsic mode functions(IMF)are selected,and the multi-scale per⁃mutation entropy and energy entropy are extracted,as the sensitive features in vibration data.Furthermore,the extracted multi-dimensional vector matrix is loaded into the support vector machine(SVM)whose kernel func⁃tion is the radial basis function(RBF),and the accurate incipient fault diagnosis for cam-driven absolute gravi⁃meter is realized based on data.Experimental results show that the proposed method can effectively diagnose various incipient faults for cam-driven absolute gravimeter,and the accuracy of fault diagnosis reaches up to 97.1%.This method solves the problem of low measurement precision in cam-driven absolute gravimeter due to incipient fault,and the incipient fault can be quickly and accurately traced.Meanwhile,it can realize the fast trac⁃ing and accurate positioning of the incipient fault for the gravimeter,and has a good prospect for engineering ap⁃plication.

关 键 词：凸轮式绝对重力仪 故障诊断 改进集总平均经验模态分解 能量熵 多尺度排列熵 

分 类 号：TH762[机械工程—仪器科学与技术]