基于HHT和模糊C均值聚类的轴向柱塞泵故障识别  

HHT and fuzzy C-means clustering-based fault recognition for axial piston pump

作　　者：姜万录[1,2] 卢传奇 朱勇[1,2] 

机构地区：[1]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 [2]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：《吉林大学学报（工学版）》2015年第2期429-436,共8页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(51475405);'973'国家重点基础研究发展计划项目(2014CB046405);河北省自然科学基金项目(E2013203161)

摘　　要：提出了一种基于Hilbert-Huang变换(HHT)和模糊C均值聚类算法相结合的故障识别方法。利用HHT在处理非线性、非平稳信号方面的优势,对采集到的轴向柱塞泵泵壳振动加速度信号进行HHT处理。首先对信号分别进行经验模态分解(EMD)和集总经验模态分解(EEMD),结合短时最大熵谱分析选取对故障最为敏感的固有模态函数(IMF)分量,再对其分别进行二次分解。然后,采用本文提出的基于局部边际能量谱特征能量的方法求出故障特征向量。最后,采用模糊C均值聚类算法进行故障模式识别。识别结果表明:EEMD比EMD在迭代次数上大幅减少,故障识别准确率有了显著提高。A fault recognition method based on Hilbert-Huang Transform（HHT）and the fuzzy Cmeans clustering is proposed.This method takes advantages of HHT in processing nonlinear and nonstationary signals.The vibration acceleration signals collected from the axial piston pump housing are processed by HHT.First,the signals are treated by means of the Empirical Mode Decomposition（EMD）and the Ensemble Empirical Mode Decomposition（EEMD）respectively.Then,the Intrinsic Mode Function（IMF）,which is most sensitive to faults,is chosen according to the short-term maximum entropy spectrum analysis,and the sensitive IMF is re-decomposed.Third,the fault characteristic vector is constructed based on the characteristic energy of the local marginal energy spectrum.Finally,the faults are recognized using the fuzzy C-means clustering algorithm.The resultsshow that,compared with EMD,the interactive time based on EEMD is reduced greatly,and the fault recognition accuracy is substantially improved by the proposed recognition method.

关 键 词：流体传动与控制 集总经验模态分解 短时最大熵谱分析 特征向量 模糊C均值聚类 

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]