辛几何模态分解和广义形态分形维数的液压泵故障诊断  

Hydraulic pump fault diagnosis method of symplectic geometry mode decomposition and generalized morphological fractal dimensions

作　　者：郑直 王宝中 刘佳鑫 姜万录[2,3] ZHENG Zhi;WANG Baozhong;LIU Jiaxin;JIANG Wanlu(College of Mechanical Engineering, North China University of Science and Technology, Tangshan 063210, China;Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China;Key Laboratory of Advanced Forging & Stamping Technology and Science, Ministry of Education of China, Yanshan University, Qinhuangdao 066004, China)

机构地区：[1]华北理工大学机械工程学院,河北唐山063210 [2]燕山大学河北省重型机械流体动力传输与控制重点实验室,河北秦皇岛066004 [3]燕山大学先进锻压成形技术与科学教育部重点实验室,河北秦皇岛066004

出　　处：《哈尔滨工程大学学报》2020年第5期724-730,共7页Journal of Harbin Engineering University

基　　金：国家自然科学基金项目(51875498);河北省省属高等学校基本科研业务费研究项目(JQN2019004);华北理工大学博士科研启动基金项目(0088/28412499);河北省自然科学基金项目(E2018203339,E2017203115)。

摘　　要：针对液压泵故障诊断问题,本文提出了一种基于辛几何模态分解和广义形态分形维数相结合的方法。对实测液压泵多模态故障振动信号进行分解;基于所提出的能量选取法,重构含有丰富运行特征信息的模态分量,并将其作为数据源;基于数据源提取,实现对液压泵不同故障的诊断。通过对比分析仿真和实测液压泵故障振动信号的试验结果,验证了该方法可以有效地诊断液压泵不同故障。Aiming at the fault diagnosis of hydraulic pumps,we propose a new fusion method based on symplectic geometry mode decomposition(SGMD)and general morphological fractal dimensions(GMFDs).First,SGMD is applied to decompose the multi-mode vibration fault signals of the hydraulic pump.Second,the modes with rich running feature information can be selected by the proposed energy method,and they are restructured as data sources.Lastly,GMFDs are extracted from the data source,and hydraulic pump faults can be diagnosed.The simulation signals and actually measured fault signals of the vibrating hydraulic pump were compared,and the ability of the proposed method to effectively diagnose hydraulic pump faults was verified.

关 键 词：液压泵 故障诊断 辛几何模态分解 广义形态分形维数 模态能量 特征提取 滑靴故障 松靴故障 

分 类 号：TH137[机械工程—机械制造及自动化] TP277[自动化与计算机技术—检测技术与自动化装置]