液压与气动Issue(1)：61-64,4.DOI:10.11832/j.issn.1000-4858.2018.01.010
基于核超限学习机的轴向柱塞泵故障诊断
Fault Diagnosis Based on Kernel Extreme Learning Machine for Axial Piston Pump
摘要
Abstract
The complex internal structure of the piston pump and the coupling between structures result in the increasing difficulty of the piston pump's fault diagnosis.In order to improve the reliability and diagnostic speed of the algorithm,the method of combining the kernel function and the extreme learning machine is used to diagnose the fault of piston pump.Firstly,the vibration and flow rate signal of the pump under normal and different fault conditions are collected by an accelerometer and a flowmeter,and the wavelet packet decomposition is used to remove the noise.Then,a total of 8-dimensional characteristic vectors are extracted,including the time domain dimensionless index,the maximum energy of band energy decomposed by the wavelet packet and the flow value of the flowmeter in the system.Finally,the kernel extreme learning machine is used to identify and diagnose 4 kinds of faults (slipper abrasion,valve plate abrasion,central spring failure,and loose slipper fault).The experimental results show that compared with the extreme learning machine,the traditional intelligent diagnosis algorithm support vector machine and the back propagation neural network,the kernel extreme learning machine has obvious advantages in fault diagnosis.关键词
故障诊断/核函数/超限学习机/轴向柱塞泵Key words
fault diagnosis/kernel function/extreme learning machine/axial piston pump分类
机械制造引用本文复制引用
曾祥辉,兰媛,黄家海,胡晋伟,魏晋宏,武兵..基于核超限学习机的轴向柱塞泵故障诊断[J].液压与气动,2018,(1):61-64,4.基金项目
国家自然科学基金(51405327) （51405327）
山西省科技成果转化与推广计划项目(20051002) （20051002）
