用于加工中心的计算机智能监测控制方法研究
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摘要
随着现代工业生产自动化、连续化水平的不断提高,加工中心的占有率也在增大,在生产中已经占有重要地位。加工中心在许多企业中被用于重要的加工环节,如果出现故障后不能及时正确地进行故障诊断和维修,则会带来较大的经济损失。随着粗集理论近年来在智能信息处理研究领域获得了迅速发展,它基于现实的大数据集,从中推理、发现知识和分类系统的某些特点,对于研究不精确知识的表达、学习、归纳方面有其独特之处。
本文研究了基于数据挖掘的加工中心故障诊断方法,跟以往的故障诊断方法不同,研究的方向并不是基于机械振动分析,而是采用了粗集理论结合神经网络的方法。
论文研究了粗集对故障数据进行约简的可行性,并应用自组织映射神经网络的聚类功能,来实现连续属性值离散化的方法;通过对诊断信息的分析,采取常规约简方法,该方法实现了样本条件属性的约简,可消除样本数据中的冗余信息。采用MATLAB神经网络工具箱建立了加工中心故障类型的智能混合诊断系统;研究了智能混合故障诊断系统,并进行了功能模块设计,各功能模块分别为:数据采集模块,数据预处理模块,数据约简模块,神经网络模块,故障诊断模块。在此基础上构建了一个基于粗集-神经网络的智能混合故障诊断系统。
With the development of automation and the high demand of reliableness, Machining Center has got important status and predomination in manufacturing. Machining Center has grown as key and deciding factor in many plants. Without timely fault diagnosis and service, serious economic loss can be caused. Rough Sets theory has made fast progress in recent years, it has outstanding ability in research of expressing, learning , concluding non-precise knowledge. It is based on practical large data sets, and deduces, find the knowledge and key of the classification systems.
So this paper studies a method of Machining Center fault diagnosis based on Rough Sets theory, which is one of the latest tools in Data Mining area. Not like the usual methods that based on mechanical vibrancy, this method combines the Rough Sets theory with the Artificial Neural Network.
The practicality of using rough set to reduce the date was discussed, in this paper, and the interval-valued continuous attribute discretization by applying self-organizing map neural network clustering was proposed, too. This article proposes a normal concision's method, which reduces the example's condition attribute and eliminates the redundant information of the date. What's more, it provides the methods used to diagnosis Machining Center's faults based on the intelligence hybrid system by adopting the MATLAB neural network workbox. At last, the functional modules which make up into intelligence hybrid system for fault diagnosis was introduced, including: data acquisition module, date preprocessor module, date reduction module, neural network module and fault diagnosis module. Herein, intelligence hybrid system based on rough sets and neural network for fault diagnosis is established in this paper.
本文研究了基于数据挖掘的加工中心故障诊断方法,跟以往的故障诊断方法不同,研究的方向并不是基于机械振动分析,而是采用了粗集理论结合神经网络的方法。
论文研究了粗集对故障数据进行约简的可行性,并应用自组织映射神经网络的聚类功能,来实现连续属性值离散化的方法;通过对诊断信息的分析,采取常规约简方法,该方法实现了样本条件属性的约简,可消除样本数据中的冗余信息。采用MATLAB神经网络工具箱建立了加工中心故障类型的智能混合诊断系统;研究了智能混合故障诊断系统,并进行了功能模块设计,各功能模块分别为:数据采集模块,数据预处理模块,数据约简模块,神经网络模块,故障诊断模块。在此基础上构建了一个基于粗集-神经网络的智能混合故障诊断系统。
With the development of automation and the high demand of reliableness, Machining Center has got important status and predomination in manufacturing. Machining Center has grown as key and deciding factor in many plants. Without timely fault diagnosis and service, serious economic loss can be caused. Rough Sets theory has made fast progress in recent years, it has outstanding ability in research of expressing, learning , concluding non-precise knowledge. It is based on practical large data sets, and deduces, find the knowledge and key of the classification systems.
So this paper studies a method of Machining Center fault diagnosis based on Rough Sets theory, which is one of the latest tools in Data Mining area. Not like the usual methods that based on mechanical vibrancy, this method combines the Rough Sets theory with the Artificial Neural Network.
The practicality of using rough set to reduce the date was discussed, in this paper, and the interval-valued continuous attribute discretization by applying self-organizing map neural network clustering was proposed, too. This article proposes a normal concision's method, which reduces the example's condition attribute and eliminates the redundant information of the date. What's more, it provides the methods used to diagnosis Machining Center's faults based on the intelligence hybrid system by adopting the MATLAB neural network workbox. At last, the functional modules which make up into intelligence hybrid system for fault diagnosis was introduced, including: data acquisition module, date preprocessor module, date reduction module, neural network module and fault diagnosis module. Herein, intelligence hybrid system based on rough sets and neural network for fault diagnosis is established in this paper.
引文
[1]赵翔,李著信,萧德云.故障诊断技术的研究现状与发展趋势[J].机床与液压,2002,(4):3-6
[2]吴今培,肖健华.智能故障诊断与专家系统[M].北京:科学出版社,1997,8-17
[3]姚勇,刘斌.KDD技术及其在故障诊断中的应用[J].信息技术,2003,27(2):69-71
[4]Finn Vidar Larsen,Comparative analysis Of Data ming Tools[J].Doctoral Thesis.Electrical engneering and computer systems and telematrics,Norwegian university of science and technology,Norway,1996
[5]Fayyad U,Piatetsky-Shapiro G,Srnyth P.The KDD process for extracting useful knowledge from volumes of data[J].Communications of the ACM,1996,39(11):27-34
[6]Clark Glymour,David Madigan,Daryl Pregibon,Padhraic Smyth Statistical Themes and Lessons for Data Mining[J].Data mining and knowledge discovery,1996,25-42
[7]朱明.数据挖掘[M].合肥:中国科学技术大学出版社,2002
[8]V T.Raisinghani.Cleaning methods in data ousing[J].KR School of Information Technology liT,Bombay,MA Report,1999.12
[9]Steve R.Gunn.Support vector machines for classification and regression:Technical reports.Faculty of engineering and applied science department of electronics and computer science[J].university of south Campton.1998.5
[10]Sushmita mitra,Sankar K.Pal,Pabitra Mitra.Data Mining in Soft Computing Framework A Survey.IEEE trans[J].On neural networks,vol13 nol,2002,January,pp3-14
[11]Han J,Fu Y Exploration of the Power of Attribute-Oriented Induction in Data Mining.In:Fayyad U M,Piatetsky-Shapiro G,Smyth P,Uthurusamy R,eds.Advances in Knowledge Discovery and Data Mining[J],AAAI/MIT Press,1996,399-421
[12]Alex A.Freitas.Understanding the Crucial Differences Between Classification and Discovery of Association Rules A Position Paper[J].ACM SIGKDD explorations.July 2000,val2,1,ppl-5
[13]Ming Dong,a new measure of classifiability and its applications[J].Department of electrical and computer engineering and computer science of the college of engineering,University of Cincinnati,2001
[14]Shoji Hirano,Shusaku Tsumoto.Indiscernibility degree of objects for evaluating simplicity of knowledge in the clustering procedure[J].ICDM 2001:pp 211-217
[15]Alex A.Freitas.On Rule Interestingness Measures[J].Knowledge-Based Systems,12(5-6):309-315,October 1999
[16]khan N.Ziarko W.Data-based acauisition and incremental modification of classification rules[J].Computational Intelligence,1995,11:357-370
[17]Slowinski R,et al.Rough set reasoning about uncertain data[J].Fundamental Informaticae,1996,27(2,3):229-243
[18]郝丽娜,王伟,吴光宇,等.粗糙集—神经网络故障诊断方法研究[J].东北大学学报(自然科学版).2003,3(24):252-255
[19]郝丽娜,徐心和.粗糙集理论在智能故障诊断中的应用[J].中国机械工程.2002.11(13):1856-1858
[20]盛颂恩,劳佳锋,陈久军.基于粗集理论的故障规则自动获取系统的研究[J].浙江工业大学学报,2004,32(2):203-207
[21]冯志鹏,杜金莲.粗糙集与神经网络集成在故障诊断中应用研究[J].大连理工大学学报,2003,43(1):70-76
[22]陈久军,盛颂恩.Rough集理论在故障诊断专家系统中的应用研究[J].机电工程,2002,19(3):49-51
[23]谭天乐,宋执环,李平.基于粗糙集的故障诊断方法[J].浙江大学学报(工学版),2003,37(1):47-50
[24]劳佳峰.应用粗集理论解决故障诊断中的不确定性问题的研究[D].硕士论文,浙江工业大学,2005
[25]曹倬璜.基于数据挖掘的设备故障诊断[D].硕士论文,北京化工大学,2005
[26]陈文伟,黄金才,赵新昱.数据挖掘技术[M].北京工业大学出版社,2002,10
[27]张文修,吴志伟,梁吉业.粗糙集理论与方法[M].北京:科学出版社,2000
[28]周庆敏,李永生,殷晨波,等.基于粗集理论的数据挖掘应用[J].南京工业大学学报,2003,3:44-47
[29]张涌钢.汽车发动机性能检测与故障诊断专家系统的研究[D].硕十论文,浙江大学,2001,5
[30]杨德华,刘家声.汽车检测与诊断技术[M].江苏科学技术出版社,1994,10
[31]侯利娟,王国,吴渝,等.粗糙集理论中的离散化问题[J].计算机科学,2000,27(12):89-94
[32]谢川,倪世宏.基于粗糙集的飞行数据模式特征提取[J].计算机工程,2005,31(12):169-171
[33]Tvo D,Gunther G.Uncertainty measures of rough set predicaion[J].Artifical Intelligence,1998,106:109-137
[34]曾黄麟.粗集理论及其应用—关于数据推理的新方法[M].重庆:重庆大学出版社,1996
[35]余雪丽,孙承意,冯秀芳.神经网络与实例学习[M].中国铁道出版社,1996,6
[36]赵永玲.基于神经网络的控制系统故障诊断研究[J].大庆石油学院,2003,2
[37]虞和济,陈长征,张省.基于神经网络的智能诊断[M].冶金工业出版社,2000
|38]郭晶,杨章玉.MATLAB6.5辅助神经网络分析与设计[M].北京:电子工业出版社,2003
[39]楼顺天,施阳.基于MATLAB的系统分析与设计[M].西安电子科技大学出版社,1999
[40]许东,吴铮.基于MATLAB6.x的系统分析与设计——神经网络[M].西安电子科技大学出版社,2002,9
[41]周志华,曹存根.神经网络及其应用[M].北京:清华大学出版社,2004
[42]吴今培,肖建华.智能故障诊断与专家系统[M].北京:科学出版社,1997
[43]苏金明,王永利.MATLAB 7.0实用指南[M].北京:电子工业出版社,2004
[44]杨乐平,李海涛,赵勇,等.LabVIEW高级程序设计[M].北京:清华大学出版社,2003
[45]杨乐平,李海涛,赵勇.LabVIEW程序设计与应用[M].北京:电子工业出版社,2004
[46]李增芳.基于人工智能和虚拟仪器技术的发动故障诊断专家系统研究[D].硕士论文,浙江大学,
[2]吴今培,肖健华.智能故障诊断与专家系统[M].北京:科学出版社,1997,8-17
[3]姚勇,刘斌.KDD技术及其在故障诊断中的应用[J].信息技术,2003,27(2):69-71
[4]Finn Vidar Larsen,Comparative analysis Of Data ming Tools[J].Doctoral Thesis.Electrical engneering and computer systems and telematrics,Norwegian university of science and technology,Norway,1996
[5]Fayyad U,Piatetsky-Shapiro G,Srnyth P.The KDD process for extracting useful knowledge from volumes of data[J].Communications of the ACM,1996,39(11):27-34
[6]Clark Glymour,David Madigan,Daryl Pregibon,Padhraic Smyth Statistical Themes and Lessons for Data Mining[J].Data mining and knowledge discovery,1996,25-42
[7]朱明.数据挖掘[M].合肥:中国科学技术大学出版社,2002
[8]V T.Raisinghani.Cleaning methods in data ousing[J].KR School of Information Technology liT,Bombay,MA Report,1999.12
[9]Steve R.Gunn.Support vector machines for classification and regression:Technical reports.Faculty of engineering and applied science department of electronics and computer science[J].university of south Campton.1998.5
[10]Sushmita mitra,Sankar K.Pal,Pabitra Mitra.Data Mining in Soft Computing Framework A Survey.IEEE trans[J].On neural networks,vol13 nol,2002,January,pp3-14
[11]Han J,Fu Y Exploration of the Power of Attribute-Oriented Induction in Data Mining.In:Fayyad U M,Piatetsky-Shapiro G,Smyth P,Uthurusamy R,eds.Advances in Knowledge Discovery and Data Mining[J],AAAI/MIT Press,1996,399-421
[12]Alex A.Freitas.Understanding the Crucial Differences Between Classification and Discovery of Association Rules A Position Paper[J].ACM SIGKDD explorations.July 2000,val2,1,ppl-5
[13]Ming Dong,a new measure of classifiability and its applications[J].Department of electrical and computer engineering and computer science of the college of engineering,University of Cincinnati,2001
[14]Shoji Hirano,Shusaku Tsumoto.Indiscernibility degree of objects for evaluating simplicity of knowledge in the clustering procedure[J].ICDM 2001:pp 211-217
[15]Alex A.Freitas.On Rule Interestingness Measures[J].Knowledge-Based Systems,12(5-6):309-315,October 1999
[16]khan N.Ziarko W.Data-based acauisition and incremental modification of classification rules[J].Computational Intelligence,1995,11:357-370
[17]Slowinski R,et al.Rough set reasoning about uncertain data[J].Fundamental Informaticae,1996,27(2,3):229-243
[18]郝丽娜,王伟,吴光宇,等.粗糙集—神经网络故障诊断方法研究[J].东北大学学报(自然科学版).2003,3(24):252-255
[19]郝丽娜,徐心和.粗糙集理论在智能故障诊断中的应用[J].中国机械工程.2002.11(13):1856-1858
[20]盛颂恩,劳佳锋,陈久军.基于粗集理论的故障规则自动获取系统的研究[J].浙江工业大学学报,2004,32(2):203-207
[21]冯志鹏,杜金莲.粗糙集与神经网络集成在故障诊断中应用研究[J].大连理工大学学报,2003,43(1):70-76
[22]陈久军,盛颂恩.Rough集理论在故障诊断专家系统中的应用研究[J].机电工程,2002,19(3):49-51
[23]谭天乐,宋执环,李平.基于粗糙集的故障诊断方法[J].浙江大学学报(工学版),2003,37(1):47-50
[24]劳佳峰.应用粗集理论解决故障诊断中的不确定性问题的研究[D].硕士论文,浙江工业大学,2005
[25]曹倬璜.基于数据挖掘的设备故障诊断[D].硕士论文,北京化工大学,2005
[26]陈文伟,黄金才,赵新昱.数据挖掘技术[M].北京工业大学出版社,2002,10
[27]张文修,吴志伟,梁吉业.粗糙集理论与方法[M].北京:科学出版社,2000
[28]周庆敏,李永生,殷晨波,等.基于粗集理论的数据挖掘应用[J].南京工业大学学报,2003,3:44-47
[29]张涌钢.汽车发动机性能检测与故障诊断专家系统的研究[D].硕十论文,浙江大学,2001,5
[30]杨德华,刘家声.汽车检测与诊断技术[M].江苏科学技术出版社,1994,10
[31]侯利娟,王国,吴渝,等.粗糙集理论中的离散化问题[J].计算机科学,2000,27(12):89-94
[32]谢川,倪世宏.基于粗糙集的飞行数据模式特征提取[J].计算机工程,2005,31(12):169-171
[33]Tvo D,Gunther G.Uncertainty measures of rough set predicaion[J].Artifical Intelligence,1998,106:109-137
[34]曾黄麟.粗集理论及其应用—关于数据推理的新方法[M].重庆:重庆大学出版社,1996
[35]余雪丽,孙承意,冯秀芳.神经网络与实例学习[M].中国铁道出版社,1996,6
[36]赵永玲.基于神经网络的控制系统故障诊断研究[J].大庆石油学院,2003,2
[37]虞和济,陈长征,张省.基于神经网络的智能诊断[M].冶金工业出版社,2000
|38]郭晶,杨章玉.MATLAB6.5辅助神经网络分析与设计[M].北京:电子工业出版社,2003
[39]楼顺天,施阳.基于MATLAB的系统分析与设计[M].西安电子科技大学出版社,1999
[40]许东,吴铮.基于MATLAB6.x的系统分析与设计——神经网络[M].西安电子科技大学出版社,2002,9
[41]周志华,曹存根.神经网络及其应用[M].北京:清华大学出版社,2004
[42]吴今培,肖建华.智能故障诊断与专家系统[M].北京:科学出版社,1997
[43]苏金明,王永利.MATLAB 7.0实用指南[M].北京:电子工业出版社,2004
[44]杨乐平,李海涛,赵勇,等.LabVIEW高级程序设计[M].北京:清华大学出版社,2003
[45]杨乐平,李海涛,赵勇.LabVIEW程序设计与应用[M].北京:电子工业出版社,2004
[46]李增芳.基于人工智能和虚拟仪器技术的发动故障诊断专家系统研究[D].硕士论文,浙江大学,