基于数据挖掘技术的故障测试与诊断方法研究
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摘要
本学位论文以数据挖掘、虚拟仪器和信息融合为理论基础,对复杂系统的故障诊断方法及其应用进行了深入的研究。该研究对保证大型复杂系统高效、安全、稳定、可靠地运行,提高设备生产效率和管理水平,最大程度恢复故障设备并挽回故障造成的经济损失,满足国民经济又好又快的发展等方面有着十分重要的现实意义。
本文设计完成了基于虚拟仪器的故障数据采集实验,为后续研究提供了充足数据。针对复杂系统的实际情况,重点研究了粗糙集算法在属性约简和特征提取中的应用,决策树算法在挖掘故障规则中的应用以及聚类算法在增量数据中挖掘新故障规则的应用。通过改进粗糙集属性约简算法,解决了其在属性约简中效率较低的问题;通过利用嵌入式SQL直接对故障数据库进行高效的数据查询与处理,大大提高了ID3算法的效率和可实现性;通过利用ART2算法与K-means算法相结合的方法,有效抑制了ART2聚类中心的漂移。同时研究了故障诊断中的信息融合,通过对诊断数据融合,使得诊断数据更加全面;通过对诊断方法的融合,使得诊断结果更加快速、准确和可靠。
最后以此三种算法为核心,以液压系统故障诊断为应用背景,设计实现了一个故障测试诊断系统。通过各功能模块的综合使用,实现了故障数据的采集、存储、预处理、规则挖掘、故障诊断和报表生成打印等一系列功能。通过对设备故障实际诊断的实验,证明该系统可以准确地进行故障诊断,同时具有快速自学习发现新规则的能力,能较好的满足实际故障诊断的需要。
This thesis taking data mining, virtual instrument and information fusion as the theoretical bases, has studied deeply on complicated system's fault diagnosis methods and its application. The researches ensure the large-scale complicated system running effectively, safely, stably and reliably, raise the equipment production efficiency and the management level, restore the fault equipment and retrieve the economic loss caused by the fault to the greatest extent, and have the very vital practical significance on ensuring sound and rapid growth of the national economy and other aspects.
This paper designs and completes fault data acquisition experiment based on the virtual instrument, which provides the sufficient data for the following research. For actual situation of complicated system, this thesis mainly studies the application of attributes reduction and feature extraction based on rough set, the application of fault rules mining based on decision tree algorithm, and the application of the new fault rules mining based on clustering algorithm in the incremental data. An improved algorithm of rough sets is presented, which solves the problem of low efficiency in the attribute reduction. An improved ID3 algorithm is presented by querying and processing database directly and efficiently using the embedded SQL, which improves the efficiency and feasibility. An improved clustering algorithm is presented by combination ART2 algorithm with K-means algorithm, which restrains the ART2 cluster center drifting effectively. At the same time, this paper studies information fusion in the fault diagnosis, through the data fusion, making the data more comprehensive, through the method fusion, making diagnosises more rapid, accurate and reliable.
At last, taking the three algorithms as the core and fault diagnosis of the hydraulic system as the application background, a fault testing and diagnosis system has been designed and realized. Through various functional modules’synthesis use, it realizes a series of function of data acquisition, storage, pretreatment, fault rules mining and fault matching, the report generation printing . Through actual experiment of fault diagnosis, it has proved that this system can diagnose accurately and discover the new fault rules quickly by self-learning, which satisfies the actual need of fault diagnosis.
本文设计完成了基于虚拟仪器的故障数据采集实验,为后续研究提供了充足数据。针对复杂系统的实际情况,重点研究了粗糙集算法在属性约简和特征提取中的应用,决策树算法在挖掘故障规则中的应用以及聚类算法在增量数据中挖掘新故障规则的应用。通过改进粗糙集属性约简算法,解决了其在属性约简中效率较低的问题;通过利用嵌入式SQL直接对故障数据库进行高效的数据查询与处理,大大提高了ID3算法的效率和可实现性;通过利用ART2算法与K-means算法相结合的方法,有效抑制了ART2聚类中心的漂移。同时研究了故障诊断中的信息融合,通过对诊断数据融合,使得诊断数据更加全面;通过对诊断方法的融合,使得诊断结果更加快速、准确和可靠。
最后以此三种算法为核心,以液压系统故障诊断为应用背景,设计实现了一个故障测试诊断系统。通过各功能模块的综合使用,实现了故障数据的采集、存储、预处理、规则挖掘、故障诊断和报表生成打印等一系列功能。通过对设备故障实际诊断的实验,证明该系统可以准确地进行故障诊断,同时具有快速自学习发现新规则的能力,能较好的满足实际故障诊断的需要。
This thesis taking data mining, virtual instrument and information fusion as the theoretical bases, has studied deeply on complicated system's fault diagnosis methods and its application. The researches ensure the large-scale complicated system running effectively, safely, stably and reliably, raise the equipment production efficiency and the management level, restore the fault equipment and retrieve the economic loss caused by the fault to the greatest extent, and have the very vital practical significance on ensuring sound and rapid growth of the national economy and other aspects.
This paper designs and completes fault data acquisition experiment based on the virtual instrument, which provides the sufficient data for the following research. For actual situation of complicated system, this thesis mainly studies the application of attributes reduction and feature extraction based on rough set, the application of fault rules mining based on decision tree algorithm, and the application of the new fault rules mining based on clustering algorithm in the incremental data. An improved algorithm of rough sets is presented, which solves the problem of low efficiency in the attribute reduction. An improved ID3 algorithm is presented by querying and processing database directly and efficiently using the embedded SQL, which improves the efficiency and feasibility. An improved clustering algorithm is presented by combination ART2 algorithm with K-means algorithm, which restrains the ART2 cluster center drifting effectively. At the same time, this paper studies information fusion in the fault diagnosis, through the data fusion, making the data more comprehensive, through the method fusion, making diagnosises more rapid, accurate and reliable.
At last, taking the three algorithms as the core and fault diagnosis of the hydraulic system as the application background, a fault testing and diagnosis system has been designed and realized. Through various functional modules’synthesis use, it realizes a series of function of data acquisition, storage, pretreatment, fault rules mining and fault matching, the report generation printing . Through actual experiment of fault diagnosis, it has proved that this system can diagnose accurately and discover the new fault rules quickly by self-learning, which satisfies the actual need of fault diagnosis.
引文
[1]王仲生.智能故障诊断与容错控制[M] .西北工业大学出版社.2005.
[2]周东华,叶银忠.现代故障诊断与容错控制[M] .清华大学出版社.2000.
[3]马学知.基于虚拟仪器的齿轮故障测试系统[D].湖南大学. 2007.
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[5] Han Jiawei,Micheline Kamber. Data Mining Concepts and Techniques[M]. Morgan Kaufmann Publishers.2000.
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[7] Han Yilun. Study of a fault diagnosis expert system for synthetic mining system hydraulic support[J]. Journal of Coal Science&Engineering. 2000,Vo1.6 No.l.
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[9] Efendic H, Delre L.Automatic iterative fault diagnosis approach for complex systems[J]. Transactions on Systems. 2006, 5(2):360-367.
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[38]徐义田.分类问题中特征选择算法的研究[J].中国农业大学. 2002
[39]冯兴杰.聚类方法及应用研究[D] .南开大学. 2004.
[40] Xiao-Dong Qian,Zhen-ou Wang,Yu Wang .A Method of Data Clustering Based on Improved Algorithm of ART2[J],0-7803-9091-1?2005 IEEE .
[41]屠燕春,郭爱煌.无线传感器网络的路由算法与仿真实现[J].计算机工程.2006, 32(22): 124-126.
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[43]雷霖,陈锋,代传龙等.基于Rough Sets的传感器异常数据处理[J].电子科技大学学报.2006, 35(4): 678-681.
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[45] Ren C. Luo,Chih-Chen Yih,Kuo Lan Su. Multisensor Fusion and Integration: Approaches,Applications, and Future Research Directions. IEEE Sensors Journal.2002, 2(2):107~119
[46] Tom M,Mitchell. Machine Leaning[M].China Machine Press.2003.
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[49] Gang Niu,TianHan, Bo-SukYang etal. Multi-agent decision fusion for motor fault diagnosis[J].Mechanical Systems and Signal Processing.2007,21:1285-1299.
[50]姜万录,李冲祥,刘继刚.多传感器数据融合技术的现状及展望[J].机床与液压.2003,3: 1 6- 1 8, 249.
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[52]齐继阳,竺长安.设备故障智能诊断方法的研究[J].仪器仪表学报.2006, 27 (10):34-36.
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[2]周东华,叶银忠.现代故障诊断与容错控制[M] .清华大学出版社.2000.
[3]马学知.基于虚拟仪器的齿轮故障测试系统[D].湖南大学. 2007.
[4] David Hand,Heikki Mannila,Padhraic Smyth, Principles of Data Mining[M]. Massachusetts The MIT Press.2001.
[5] Han Jiawei,Micheline Kamber. Data Mining Concepts and Techniques[M]. Morgan Kaufmann Publishers.2000.
[6] Liu Xianghong,He Yigang. Data Mining-Based Fault Detect and Diagnosis For the Video Amplifier Circuit[C]. Proceedings of the 2003 IEEE International Conference on Robotics, Intelligent Systems and Signal Processing Changsha, China - October 2003:1179-184.
[7] Han Yilun. Study of a fault diagnosis expert system for synthetic mining system hydraulic support[J]. Journal of Coal Science&Engineering. 2000,Vo1.6 No.l.
[8] Bocaniala C D, Costa S D, Palade V. Fuzzy-based refinement of the fault diagnosis task in industrial devices[J]. Journal of Intelligent Manufacturing. 2005, 16(6):599-614.
[9] Efendic H, Delre L.Automatic iterative fault diagnosis approach for complex systems[J]. Transactions on Systems. 2006, 5(2):360-367.
[10] Nath S, Chakrabarti A, Mukhopadhyay A K. A microcomputer-based unique digital fault diagnosis scheme of radial transformer feeders. IEEE Transactions on Power Delivery. 2006,21(4):1824-1829.
[11]孙卫祥.基于数据挖掘与信息融合的故障诊断方法研究[D].上海交通大学2006.
[12]侯国屏,王砷,叶齐鑫.LabVIEW7.1编程与虚拟仪器设计[M].清华大学出版社.2005.
[13] Horst Ahlers.多传感器技术及其应用.王磊等译[M].国防工业出版社.2001.
[14] Hoja,Jerzy,Lentka,Grzegorz.Virtual instrument using near transformation for parameter identification of impedance objects[J]. Measurement Science Technology.2003,15(3):54-6.
[15]石红.系统故障诊断专家系统的研究与设计[D] .哈尔滨工程大学. 2000.
[16]方勇.基于C630车床数控旋压机床改造的研究[D].合肥工业大学.2007.
[17]张文修,吴伟志等.粗糙集理论与方法[M].科学出版社.2001.
[18]夏雨佳,李少远,席裕庚.一种基于粗糙集的信息系统决策规则提取方法[J].控制与决策.2001,(9):577-580.
[19]杨一展.数据挖掘技术在故障诊断中的应用研究[D].西安电子科技大学. 2008.
[20] Pawlak Z.Rough sets and intelligent data analysis[J].Information Sciences.2002,147:1-12.
[21] Pawlak Z. Rough Set-theoretical Aspects of Reasoning about Data[M]. Dordrecht: Kluwer Academic Publishers.1991.
[22] Shen L X, Francis E H, Qu L S. Fault diagnosis using rough sets theory[J].Computer in Indeustry.2000,43(3):61-72.
[23] Pawlak Z. Rough set approach to knowledge-based decision support[J].European Journal of Oprational Research.1997,99(6):48-57.
[24] Kankana Chakabarty,Ranjit Biswas and Sudarsan Nanda, Fuzziness in rough set[J]. Fuzzy Sets and Systems.2000,110,247-251.
[25] Kryszkiewicz M.Rough set approach to incomplete information systems[J]. Information Sciences.1998,11(2):39-49.
[26]王庆,巴德纯,孟祥志.基于Rough sets-C4.5的故障征兆提取与判别[J] .东北大学学报.2006(10):1138-1141.
[27] Pawlak Z. Rough sets[J].International Journal of Computer and Information Sciences.1982,11(5):341-356.
[28] Quinlan J R. Induction of decision trees[J].MachineLearning.1986,(4):81-106.
[29]陈沛玲.决策树分类算法优化研究[D] .中南大学. 2007.
[30]王莎莎,孙继银,李琳琳.故障决策树模型在诊断专家系统中的应用[J].计算机应用.2005,25 (s):293-294.
[31]丁益祥.嵌入式SQL语言在VC中的实现[J].武汉科技学院学报.2003, 8.
[32] Wu Sen,Wu Ling-yu,Long Yu. Improved Classification Algorithm by Minsup and Minconf Based on ID3[C]. Management Science and Engineering, 2006. ICMSE '06. 2006 International Conference on Oct.5 2006-Sept.7 2006:135-139
[33] Bajwa A, Kulkarni D. Engine Data Analysis Using Decision Trees[R].AIAA 2000:2000-3627.
[34]罗志磊.决策树方法在高考志愿分析中的应用研究[D].河南大学. 2007.
[35] Jearanaitanakij, K. Classifying Continuous Data Set by ID3 Algorithm [C]. Information,Communications and Signal Processing,2005 Fifth InternationalConference on 06-09 Dec. 2005:1048–1051.
[36] Bajwa A, Kulkarni D. Engine Data Analysis Using Decision Trees[R].AIAA 2000:2000-3627.
[37]段霞霞.基于聚类的故障诊断技术研究[D] .西安电子科技大学. 2007.
[38]徐义田.分类问题中特征选择算法的研究[J].中国农业大学. 2002
[39]冯兴杰.聚类方法及应用研究[D] .南开大学. 2004.
[40] Xiao-Dong Qian,Zhen-ou Wang,Yu Wang .A Method of Data Clustering Based on Improved Algorithm of ART2[J],0-7803-9091-1?2005 IEEE .
[41]屠燕春,郭爱煌.无线传感器网络的路由算法与仿真实现[J].计算机工程.2006, 32(22): 124-126.
[42]张建明,宋迎清,周四望等.无线传感器网络中数据汇聚技术的研究[J].计算机应用.2006, 26(6): 1273-1278.
[43]雷霖,陈锋,代传龙等.基于Rough Sets的传感器异常数据处理[J].电子科技大学学报.2006, 35(4): 678-681.
[44] Ulakov A,Davcev D. Data Mining in Wireless Sensor NetworksBased on Artificial Neural Networks Algorithms[C]//Proc. of the 1stInternational Workshop on Data Mining in Sensor Networks of SDM. [S. l.]: SIAM Press, 2005: 10-16.
[45] Ren C. Luo,Chih-Chen Yih,Kuo Lan Su. Multisensor Fusion and Integration: Approaches,Applications, and Future Research Directions. IEEE Sensors Journal.2002, 2(2):107~119
[46] Tom M,Mitchell. Machine Leaning[M].China Machine Press.2003.
[47]孙涛,宏建.目标识别中的信息融合技术明[J].自动化仪表.2001,22 (2): 1-4.
[48]高翔,王勇.数据融合技术综述[J].计算机测量与控制.2002, 10: 20-22.
[49] Gang Niu,TianHan, Bo-SukYang etal. Multi-agent decision fusion for motor fault diagnosis[J].Mechanical Systems and Signal Processing.2007,21:1285-1299.
[50]姜万录,李冲祥,刘继刚.多传感器数据融合技术的现状及展望[J].机床与液压.2003,3: 1 6- 1 8, 249.
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