|本期目录/Table of Contents|

[1]张家良,曹建福,高峰.结合非线性频谱与贝叶斯网络的复杂装备传动系统故障诊断[J].电机与控制学报,2014,18(03):107-112.
 ZHANG Jia-liang,CAO Jian-fu,GAO Feng. Fault diagnosis of driving system for complex equipmentbased on nonlinear spectrum and Bayesian network[J].,2014,18(03):107-112.
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结合非线性频谱与贝叶斯网络的复杂装备传动系统故障诊断(PDF)
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
18
期数:
2014年03
页码:
107-112
栏目:
出版日期:
2014-03-15

文章信息/Info

Title:
 Fault diagnosis of driving system for complex equipment
based on nonlinear spectrum and Bayesian network
作者:
 张家良 曹建福 高峰
 1. 西安交通大学机械制造系统工程国家重点实验室,陕西西安710049;
2. 西安交通大学苏州研究院,江苏苏州215123
Author(s):
 ZHANG Jia-liang CAO Jian-fuGAO Feng
 1. State Key Laboratory for Manufacturing Systems Engineering,Xi’an Jiaotong University,Xi’an 710049,China;
2. Suzhou Academy,Xi’an Jiaotong University,Suzhou 215123,China
关键词:
 非线性输出频率响应函数 贝叶斯网络 复杂装备 传动系统 故障诊断
Keywords:
 nonlinear output frequency response function Bayesian network complex equipment drivingsystem fault diagnosis
分类号:
-
DOI:
-
文献标志码:
A
摘要:
 :针对复杂装备传动系统的故障诊断问题,提出了一种非线性频谱特征与贝叶斯网络相结合
的故障诊断方法。为了克服基于Volterra 级数的广义频率响应函数所存在的计算量膨胀问题,采
用一维的非线性输出频率响应函数获取传递频谱特征。针对非线性频谱故障征兆与多故障模式之
间存在不确定性,采用朴素贝叶斯网络进行故障识别。建立了数控装备伺服传动系统模型,重点研
究了伺服传动系统永磁同步电动机定子过热与机电过载故障的诊断问题。通过前3 阶非线性频谱
特征构建了伺服传动系统贝叶斯诊断网络模型,给出了故障检测与识别的具体算法。故障诊断实
验结果表明提出方法的故障识别率高、实时性好,平均识别率和识别时间分别为97. 5% 和
0. 26 ms。
Abstract:
 A fault diagnosis approach is proposed based on nonlinear spectrum feature and Bayesian network
for complex equipment driving system. In order to overcome the problem of calculated amount expansion
of generalized frequency response function based on Volterra series,the single-dimensional nonlinear
output frequency response function is used to obtain transmission frequency spectrum feature. In
view of the uncertainty between fault symptoms of nonlinear spectra and multiple fault modes,naive
Bayesian network is used to identify fault. The model of servo drive system of numerical control equipment
was constructed and the fault diagnosis of stator overheating and overload of permanent magnet synchronous
motor were mainly studied. The Bayesian diagnosis network model was constructed based on the
features of the first three orders nonlinear frequency spectra,then the algorithm of fault detection and identification
was given. The experimental results of fault diagnosis indicate the proposed method has high
recognition rate and good timeliness,and the average recognition rate is 97. 5% and the average time is
0. 26 ms

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备注/Memo

备注/Memo:
更新日期/Last Update: 2014-11-25