基于灰理论的变频调速系统故障诊断的研究
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摘要
最优经济运行导致设备系统日趋规模化、复杂化和高度自动化。新科技的飞速发展及其应用,又大大增加了设备结构的复杂性、运行环境的独特性和诱发故障的多源性,使得故障检测、诊断、预测愈发困难,故障造成的损失和维修费用也大幅上升。故障预测研究能够有效预防事故的发生,最大程度地降低故障损失,并能提供异常现象的事件数据及原因。利用新的科学理论,探索新的诊断预测方法是故障诊断预测研究领域的一个重要方面。
灰色预测模型具有要求样本数据少、计算方便、预测精度较高并且可对结果进行校验等优点,因此被广泛应用。灰建模的累加生成运算在一定程度上强化了确定性成分,弱化了不确定性成分。由微分方程描述的灰模型能够较完整地描述被研究对象的运行行为,能够揭示系统内部事物连续发展变化的过程。但传统的GM (1,1)模型只适用于等间距序列的建模预测。实践中,经常会遇到不等间距的情况,于是一些不等距灰色建模方法被提出来,不过难免有些缺陷。其中一般累加生成差分UGM (1,1)模型中平滑值是一种等权生成,不符合数据的实际情况,因此本文在该模型的基础上提出一种平滑值权值寻优生成的RUGM (1,1)模型,使预测精度提高了10%以上,同时计算量并未明显增加。文章中通过实践指明UGM (1,1)模型应用中的一些条件及注意事项,并给出了一些建议。
本文针对故障诊断中数据采集问题,采用虚拟仪器对变频器输出信号进行采集,然后把数据导入仿真软件Matlab中计算,并针对故障诊断中特征量难以提取的问题,运用小波分析及其它先进的信号分析方法对变频器的输出信号进行了分析和故障特征的提取,指出了输出电流的歪度的绝对值和三分频下输出电压的特定频带的能量可作为变频器的故障特征。
The optimal economic system led to increasing size of equipment complicated and highly automated. The rapid development of new technology and its applications greatly increased the complexity of the structure of equipment, uniqueness of environment and multi-induced failure of endogenous, making fault detection, diagnosis, prediction more difficult, the losses caused by faults and maintenance costs have also increased significantly. Prediction of failure can effectively prevent accidents, minimize the failure losses and provide information and reasons of abnormal events. To use new scientific theories to explore new methods of diagnosis and forecast is an important aspect in field of prediction of failure diagnosis.
Grey AGO computing model to a certain extent strengthens the element of uncertainty,weakens the element of uncertainty. Grey model described by differential equations can study the activities of objects more complete,and reveal the continuous changing process of internal system. However, traditional GM (1,1) model is only applicable to pitch sequence, such as the prediction model.Non-equidistance often happens in practice,so some of the non-equidistance method of grey model is put forward, but there are inevitably some shortcomings. Smooth value of general AGO difference UGM (1,1) model is a equal power generating, data do not meet the actual situation. Therefore, in this paper, a smooth weight optimization generated RUGM(1,1) model is put forward, so that the prediction accuracy increases by more than 10%, at the same time calculation does not increase significantly, some suggestions are given on conditions and notes about application of UGM(1,1) model.
In this paper, virtual instruments are used to collect output signal of transducer, and then import the data in the simulation software Matlab for calculation,and aim at the problem which features for diagnosis is difficult to extract, using wavelet analysis and other advanced signal analysis methods on output signals for analyzing and extraction of fault features, pointing out that the absolute value of the degree of distortion of the output current and a particular band of energy of the output voltage under one-third of converter can be used as the characteristics of the fault.
灰色预测模型具有要求样本数据少、计算方便、预测精度较高并且可对结果进行校验等优点,因此被广泛应用。灰建模的累加生成运算在一定程度上强化了确定性成分,弱化了不确定性成分。由微分方程描述的灰模型能够较完整地描述被研究对象的运行行为,能够揭示系统内部事物连续发展变化的过程。但传统的GM (1,1)模型只适用于等间距序列的建模预测。实践中,经常会遇到不等间距的情况,于是一些不等距灰色建模方法被提出来,不过难免有些缺陷。其中一般累加生成差分UGM (1,1)模型中平滑值是一种等权生成,不符合数据的实际情况,因此本文在该模型的基础上提出一种平滑值权值寻优生成的RUGM (1,1)模型,使预测精度提高了10%以上,同时计算量并未明显增加。文章中通过实践指明UGM (1,1)模型应用中的一些条件及注意事项,并给出了一些建议。
本文针对故障诊断中数据采集问题,采用虚拟仪器对变频器输出信号进行采集,然后把数据导入仿真软件Matlab中计算,并针对故障诊断中特征量难以提取的问题,运用小波分析及其它先进的信号分析方法对变频器的输出信号进行了分析和故障特征的提取,指出了输出电流的歪度的绝对值和三分频下输出电压的特定频带的能量可作为变频器的故障特征。
The optimal economic system led to increasing size of equipment complicated and highly automated. The rapid development of new technology and its applications greatly increased the complexity of the structure of equipment, uniqueness of environment and multi-induced failure of endogenous, making fault detection, diagnosis, prediction more difficult, the losses caused by faults and maintenance costs have also increased significantly. Prediction of failure can effectively prevent accidents, minimize the failure losses and provide information and reasons of abnormal events. To use new scientific theories to explore new methods of diagnosis and forecast is an important aspect in field of prediction of failure diagnosis.
Grey AGO computing model to a certain extent strengthens the element of uncertainty,weakens the element of uncertainty. Grey model described by differential equations can study the activities of objects more complete,and reveal the continuous changing process of internal system. However, traditional GM (1,1) model is only applicable to pitch sequence, such as the prediction model.Non-equidistance often happens in practice,so some of the non-equidistance method of grey model is put forward, but there are inevitably some shortcomings. Smooth value of general AGO difference UGM (1,1) model is a equal power generating, data do not meet the actual situation. Therefore, in this paper, a smooth weight optimization generated RUGM(1,1) model is put forward, so that the prediction accuracy increases by more than 10%, at the same time calculation does not increase significantly, some suggestions are given on conditions and notes about application of UGM(1,1) model.
In this paper, virtual instruments are used to collect output signal of transducer, and then import the data in the simulation software Matlab for calculation,and aim at the problem which features for diagnosis is difficult to extract, using wavelet analysis and other advanced signal analysis methods on output signals for analyzing and extraction of fault features, pointing out that the absolute value of the degree of distortion of the output current and a particular band of energy of the output voltage under one-third of converter can be used as the characteristics of the fault.
引文
[1]孙丰涛.变频器故障诊断技术研究与分析.电机与控制学报,2005,9(3):272-274
[2]刘振兴,伊项根.基于Hilbert模量频谱分析的异步电动机转子故障在线检测与故障诊断方法.中国电机工程学报,2003,23(7):156-161
[3]许伯强,李和明.笼型部电动机转子断条故障检测新方法.中国电机工程学报,2004,25(5):116-120
[4]李海军.基于灰色系统理论的电力变压器故障与诊断方法的研究:[硕士论文].北京:北京交通大学,2006
[5]赵翔,李著信,萧德云.故障诊断技术的研究现状与发展趋势.机床与液压,2002,(4),3-7
[6]王江萍.机械设备故障诊断技术及应用.西安:西北工业大学出版社,2001
[7]周瑾.矿井提升机故障机理与智能诊断研究:[博士学位论文].徐州:中国矿业大学,2000
[8]伍奎.机械设备故障诊断技术的现状与展望.重庆石油高等专科学校学报,1999,1(4):18-21
[9]虞和济,陈长征,张省.基于神经网络的智能诊断.北京:冶金工业出版社,2002
[10]崔博文.电动机变频调速系统故障检测与诊断技术研究:[博士学位论文].西安:西北工业大学,2002
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[13]文小玲,尹项根.一种电力电子电路故障诊断方法.电工技术杂志,2003(7):49-51
[14] R. Spee A .K.Wallace,Remedial strategies for brushless dc drive failures, IEEE Trans. Ind Applica. Vol.27, No.2,1990
[15] G.Gentile,N.Rotondale,M.Tursini, Investigation of inverter-fed inductionmotors under fault conditions, IEEE-PESC'92
[16] K.D ebebe,V .Rajagopalan,Diagnosis and monitoring for AC d rives, Conf. Rec. IEEE-LAS1992 annual metting
[17]苏鹏声,王志强,姜建国.用人工神经网络诊断电力电子电路主回路故障.清华大学学报(自然科学版),Vol.39,No.3,1999
[18] CIRRINCIONEG,C IRRINCIONEM .Fault Diagnosis in There Phase Converters Using the Kohonen Neural Network Classifier. Speedam:Europa Press,1994
[19]马皓.基于神经网络的电力电子电路故障诊断:[博士学位论文].浙江:浙江大学,1997
[20] WANG M L, ZHANG C W. Research on fault diagnosis technology of inverter based on associative memory neural network. The Fifth World Congress on Intelligent Control and Automation. Hangzhou: ZHEJIANG University Press, 2004.15-19
[21]何成兵,顾煜炯,杨昆.一种改进模糊神经网络及其在故障诊断中的应用.振动工程学报,2003,16(1):1-4
[22]刘德开.基于神经网络的电力电子电路故障诊断:[博士学位论文].浙江:浙江大学,1997.
[23]徐德鸿,程肇基,崔学军.用傅里叶分析法诊断电力电子电路的故障.浙江大学学报,1994,28(6):666-674
[24]陈如清,李强.一种基于频谱分析的可控整流电路故障诊断方法.电机与控制学报,2005,9(1):81-85
[25]徐德鸿,程肇基,范云其.诊断电力电子电路故障的新方法-沃尔什分析法.电工技术学报,1993,11(3):32-35
[26]庞南生.基于灰色系统理论多资源网络计划分配的优化方法.运筹与管理,1997,6(1):27 -33
[27]朱克西,孙海清,饶志坚.云南省农村区域生态经济评价初探.云南农业大学学报,1997,12(2):73-76
[28]柴涛,刘玉存等交通事故的灰色预测和关联分析.华北工学院学报,1998,19(1):1-4
[29]高清廉,邱天霞等.山东省海洋渔业结构调整研究.青岛海洋大学学报,1999,29(2):215-223
[30]罗佑新,张龙庭,李敏.灰色系统理论及其在机械工程中的应用.长沙:国防科技大学出版社,2001
[31]彭运福.灰色预测——校正模型及应用.系统工程,1989,(3):3741
[32]郭晓汾,徐双应,门广维.基于灰加权关联度的公路主枢纽站场布局决策方法.中国公路学报,1997,10(3):83-88
[33]贾永全,苗树君.黑龙江垦区奶牛产业化的灰色控制.黑龙江八一农垦大学学报.1999,11(3):92-98
[34]康慕谊,姚华荣,刘硕.陕西关中地区土地资源的优化配置.自然资源学报,1999,14(4):363-367
[35]唐炎钊,邹珊刚,企业技术创新能力的多层次灰色评价科技进步与对策, 1999,16(5):46-48
[36]冯利华.灰色预测模型的问题讨论.系统工程理论与实践,1997,(12):12 5-128
[37]陈世联.灰色系统的几个理论问题.系统工程,1997,15(1):20-23
[38]王中宇,夏新涛,朱坚民.测量不确定度的非统计理论.北京:国防工业出版社,2000
[39]罗佑新,张龙庭,李敏.灰色系统理论及其在机械工程中的应用.长沙:国防科技大学出版社,2001
[40] Deng J L Properties of the grey forcasting model GM(l,1〕.In:Grey System,China Ocean Press,1988:70-78
[41]刘思峰,党耀国,方志耕等.灰色系统理论及其应用(第三版).北京:科学出版社,2004:140-146
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[2]刘振兴,伊项根.基于Hilbert模量频谱分析的异步电动机转子故障在线检测与故障诊断方法.中国电机工程学报,2003,23(7):156-161
[3]许伯强,李和明.笼型部电动机转子断条故障检测新方法.中国电机工程学报,2004,25(5):116-120
[4]李海军.基于灰色系统理论的电力变压器故障与诊断方法的研究:[硕士论文].北京:北京交通大学,2006
[5]赵翔,李著信,萧德云.故障诊断技术的研究现状与发展趋势.机床与液压,2002,(4),3-7
[6]王江萍.机械设备故障诊断技术及应用.西安:西北工业大学出版社,2001
[7]周瑾.矿井提升机故障机理与智能诊断研究:[博士学位论文].徐州:中国矿业大学,2000
[8]伍奎.机械设备故障诊断技术的现状与展望.重庆石油高等专科学校学报,1999,1(4):18-21
[9]虞和济,陈长征,张省.基于神经网络的智能诊断.北京:冶金工业出版社,2002
[10]崔博文.电动机变频调速系统故障检测与诊断技术研究:[博士学位论文].西安:西北工业大学,2002
[11] S. Catellani J. S aadi G .Champenois, Monitoring of a static converter fed machine using average reference models, I FAC Safe Process'91, Baden-Baden, G ermany,1991
[12] C.S. Berendsen, G .Champenois, J .Davoine, G .Rostaing, How to detect and to localize a fault in a dc/dc converte IEEE-IECON'92
[13]文小玲,尹项根.一种电力电子电路故障诊断方法.电工技术杂志,2003(7):49-51
[14] R. Spee A .K.Wallace,Remedial strategies for brushless dc drive failures, IEEE Trans. Ind Applica. Vol.27, No.2,1990
[15] G.Gentile,N.Rotondale,M.Tursini, Investigation of inverter-fed inductionmotors under fault conditions, IEEE-PESC'92
[16] K.D ebebe,V .Rajagopalan,Diagnosis and monitoring for AC d rives, Conf. Rec. IEEE-LAS1992 annual metting
[17]苏鹏声,王志强,姜建国.用人工神经网络诊断电力电子电路主回路故障.清华大学学报(自然科学版),Vol.39,No.3,1999
[18] CIRRINCIONEG,C IRRINCIONEM .Fault Diagnosis in There Phase Converters Using the Kohonen Neural Network Classifier. Speedam:Europa Press,1994
[19]马皓.基于神经网络的电力电子电路故障诊断:[博士学位论文].浙江:浙江大学,1997
[20] WANG M L, ZHANG C W. Research on fault diagnosis technology of inverter based on associative memory neural network. The Fifth World Congress on Intelligent Control and Automation. Hangzhou: ZHEJIANG University Press, 2004.15-19
[21]何成兵,顾煜炯,杨昆.一种改进模糊神经网络及其在故障诊断中的应用.振动工程学报,2003,16(1):1-4
[22]刘德开.基于神经网络的电力电子电路故障诊断:[博士学位论文].浙江:浙江大学,1997.
[23]徐德鸿,程肇基,崔学军.用傅里叶分析法诊断电力电子电路的故障.浙江大学学报,1994,28(6):666-674
[24]陈如清,李强.一种基于频谱分析的可控整流电路故障诊断方法.电机与控制学报,2005,9(1):81-85
[25]徐德鸿,程肇基,范云其.诊断电力电子电路故障的新方法-沃尔什分析法.电工技术学报,1993,11(3):32-35
[26]庞南生.基于灰色系统理论多资源网络计划分配的优化方法.运筹与管理,1997,6(1):27 -33
[27]朱克西,孙海清,饶志坚.云南省农村区域生态经济评价初探.云南农业大学学报,1997,12(2):73-76
[28]柴涛,刘玉存等交通事故的灰色预测和关联分析.华北工学院学报,1998,19(1):1-4
[29]高清廉,邱天霞等.山东省海洋渔业结构调整研究.青岛海洋大学学报,1999,29(2):215-223
[30]罗佑新,张龙庭,李敏.灰色系统理论及其在机械工程中的应用.长沙:国防科技大学出版社,2001
[31]彭运福.灰色预测——校正模型及应用.系统工程,1989,(3):3741
[32]郭晓汾,徐双应,门广维.基于灰加权关联度的公路主枢纽站场布局决策方法.中国公路学报,1997,10(3):83-88
[33]贾永全,苗树君.黑龙江垦区奶牛产业化的灰色控制.黑龙江八一农垦大学学报.1999,11(3):92-98
[34]康慕谊,姚华荣,刘硕.陕西关中地区土地资源的优化配置.自然资源学报,1999,14(4):363-367
[35]唐炎钊,邹珊刚,企业技术创新能力的多层次灰色评价科技进步与对策, 1999,16(5):46-48
[36]冯利华.灰色预测模型的问题讨论.系统工程理论与实践,1997,(12):12 5-128
[37]陈世联.灰色系统的几个理论问题.系统工程,1997,15(1):20-23
[38]王中宇,夏新涛,朱坚民.测量不确定度的非统计理论.北京:国防工业出版社,2000
[39]罗佑新,张龙庭,李敏.灰色系统理论及其在机械工程中的应用.长沙:国防科技大学出版社,2001
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