数据挖掘技术及在机车故障诊断中的应用
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摘要
数据挖掘研究如何从大量的数据中智能地、自动地提取出有价值的知识和信息，是当前相当活跃的研究领域。近年来，随着我国铁路信息化建设快速发展，知识的自动获取已经成为制约其进一步发展的“瓶颈”。因此，研究适用于机车故障诊断领域的数据挖掘，具有理论意义和重要的实用价值。
本文系统归纳总结了现有的数据挖掘技术，分析了粗糙集在数据挖掘应用中的特点。以SS8型电力机车主变流器为研究对象，通过对主变流器的工作原理的分析，建立了主变流器的仿真模型。以变流器的输出电压为故障信息分析对象，并仿真了变流器不同整流元件开路故障时的运行情况，构造了相应的输出电压波形。采用小波变换对电压波形进行能量分解，构造相应的特征向量。
由于特征向量的冗余，可能产生不可识别的故障。本文提出应用基于粗糙集的数据挖掘技术对变流器进行故障诊断，研究了故障特征的约简方法及诊断规则的获取方法，在保持识别能力的情况下，对规则进行修剪，得到了约简规则库。从仿真应用来看，所提出的应用方法具有较强的鲁棒性和泛化能力。
数据挖掘研究正处在发展阶段，数据挖掘本身以及其在机车故障中的应用还有许多问题值得探讨，本文的研究工作是一个尝试，相关工作还有待进一步深入。
Data Mining, a new generation of tools and techniques for automatic and intelligent database analysis, is an active area with the promise for a high payoff in many business and scientific application. On the other hand, knowledge acquisition has been a bottleneck with the rapid development of railway information technology. To deal with this challenge, Data Mining technology is studied and applied to locomotive fault diagnose in this paper.
The paper makes a summary of the current technology of Data Mining and analyses the feature of Rough Set in the application of Data Mining. Next, the SS8 Converter is listed as study target, through the analysis of the Converter's work principle, the Converter's simulated model is founded. According to Converter's output voltage for analyzed target of fault information, the paper simulates the operation of the Converter different components in disconnection fault and structures relevant output waveforms. It adopts wavelet transform to make the power decomposition to waveforms and constructs relevant character vectors.
Owing to the redundant character vector, it might cause some faults which cannot be recognized. Therefore, the paper adopts the technology based on Data Mining of Rough Set for Converter fault diagnose, study the method of fault character reduction and diagnose rule obtainer. The effective rule sets are acquired in terms of reduction of existing rules under remain identify ability conditions. When the results apply to the simulate data, it indicates all above methods have stronger robustness and general ability.
Data Mining research are on the stage of developing. A lot of problems is worthy of discussing about Data Mining itself and its application in locomotive fault. The study of this paper is just to have a try, relevant work needs further study.
本文系统归纳总结了现有的数据挖掘技术，分析了粗糙集在数据挖掘应用中的特点。以SS8型电力机车主变流器为研究对象，通过对主变流器的工作原理的分析，建立了主变流器的仿真模型。以变流器的输出电压为故障信息分析对象，并仿真了变流器不同整流元件开路故障时的运行情况，构造了相应的输出电压波形。采用小波变换对电压波形进行能量分解，构造相应的特征向量。
由于特征向量的冗余，可能产生不可识别的故障。本文提出应用基于粗糙集的数据挖掘技术对变流器进行故障诊断，研究了故障特征的约简方法及诊断规则的获取方法，在保持识别能力的情况下，对规则进行修剪，得到了约简规则库。从仿真应用来看，所提出的应用方法具有较强的鲁棒性和泛化能力。
数据挖掘研究正处在发展阶段，数据挖掘本身以及其在机车故障中的应用还有许多问题值得探讨，本文的研究工作是一个尝试，相关工作还有待进一步深入。
Data Mining, a new generation of tools and techniques for automatic and intelligent database analysis, is an active area with the promise for a high payoff in many business and scientific application. On the other hand, knowledge acquisition has been a bottleneck with the rapid development of railway information technology. To deal with this challenge, Data Mining technology is studied and applied to locomotive fault diagnose in this paper.
The paper makes a summary of the current technology of Data Mining and analyses the feature of Rough Set in the application of Data Mining. Next, the SS8 Converter is listed as study target, through the analysis of the Converter's work principle, the Converter's simulated model is founded. According to Converter's output voltage for analyzed target of fault information, the paper simulates the operation of the Converter different components in disconnection fault and structures relevant output waveforms. It adopts wavelet transform to make the power decomposition to waveforms and constructs relevant character vectors.
Owing to the redundant character vector, it might cause some faults which cannot be recognized. Therefore, the paper adopts the technology based on Data Mining of Rough Set for Converter fault diagnose, study the method of fault character reduction and diagnose rule obtainer. The effective rule sets are acquired in terms of reduction of existing rules under remain identify ability conditions. When the results apply to the simulate data, it indicates all above methods have stronger robustness and general ability.
Data Mining research are on the stage of developing. A lot of problems is worthy of discussing about Data Mining itself and its application in locomotive fault. The study of this paper is just to have a try, relevant work needs further study.
引文
[1] U. Fayyad, G. Piatetsky-Shapiro, and Padhraic Smyth, Knowledge Discovery and Data Mining:Towards a Unifying Framework. Proceedings of Second International Conference on Knowledge Discovery and Data Mining (KDD-96), AAAI Press, 1996.
[2] F. Mitchell,D.H.Sleeman, R. Milne:KA the KDD way or How to doKnowledge Acquisition without completely annoying your expert, 1999.
[3] R. Nowicki et al.: Rough Sets Analysis of Diagnostic Capacity of Viberoacoustic Symptoms, Computer Math. Applic., Vol.24,No.7,109-123,1992
[4] W. Ziarko: Variable Precision Rough Model, Joural of Computer and System 46, 39-59, 1993
[5] Z. Pawlak: Rough Sets, International J. of Computer and Sciences, Vol.11,No.5, 341-356, 1982
[6] Z. Pawlak: Rough Sets: Theoretical Aspects of Reasoning About Data. Dordrecht: Kluwer, 1991
[7] Z. Pawlak, J. Grzymala-Busse, R. Slowinski, W. Ziarko: Rough Sets, Communications of the ACM, Vol.38, No. 11, 89-95, November 1995
[8] http://www, idi.ntnu.no/～aleks/rosetta/
[9] S. Letourneau: Discovering Useful Knowledge from Aircraft Operation/Maintenance Data, Workshop on Machine Learning Applications in the Real World: Methodological Aspects and Implications. ICML97, 1997.
[10] M. Lehane, F. Dube, M. Halasz, R. Orchard, R. Wylie, M. Zaluski: Integrated Diagnostic System (IDS) for Aircraft Fleet Maintenace, http://ai.iit.nrc.ca/IR_public, 1998
[11] M. Halasz, F. Dube B. Orchard: The Integrated Diagnostic System(IDS): Remote Monitoring and Decision Support for Commercial Aircraft-Putting Theory into Practice, 1999
[12] The KATE Softwre Suite, www.acknosoft.com/ftools.html, 1999,10
[13] Nowicki R. Slowinski R. Jermy E,Evaluation of vibroacoustic diagnostic sy mptoms by means of the rough sets theory [J] Computers in Industry. 1997. 20:141—152
[14] Pawlak Z Rough sets [J] International Journal of Information and Computer Science. 2000. 11 (5): 341—348
[15] Pawlak Z,Reasoning about data --A rough set prespective,LNAI 1424,Proceeding of RSCTC 98 [C],Warsaw;Springer, 1998
[16] Pawlak Z. Busse J G. Slowinski R. et al Rough sets [J] Communcations Of the ACM, 1999, 38 (11): 89～95
[17] Johnson S D. Approximation algorithms for combinatorial problems. Journal of Computer and System Sciences, 1974(9):256-278
[18] Andrzej Skowron, Hung Son Nguyen, Bool reasoning scheme with some application in data mining. PKDD, 1999
[19] Holland, J. H., Genetic algorithms and the optimal allocations of trials, SIAM Journal of Computing, 2,88-105,1973
[20] hrn A. Discernibility and Rough Sets in Medicine: Tools and Applications: [PhD thesis]. Trondheim, Norway: Norwegian university of science and Technology, Department of Computer and Information Science, 1999
[21] Bhavani Thuraisingham, A primer for understanding and applying data mining [J],IT Professional,2000,2(1):28-31.
[22] Christ M. Atkinson. etal, Virtual Sensing: A Neural Network Based On Intelligent Performance and Emission Prediction System for On Board Diagnose and Engine Control SAE 980516
[23] Cristina Olaru, Louis Wehenkel. Data mining [J].IEEE Computer Applications in Power,1999,12(1): 19-25.
[24] Fayyad U, Piatetsky- Shapiro G, Smyth R, From Data mining to Knowledge Discovery; An Overview, In; Faygad U, ed, Advances in Knowledge Discovery and Data Mining[M],AAAI Press/The MT Press, 1996,1-34.
[25] Wong S K M, Ziarko W. On optimal decision rules in decision tables. Bulletin of Polish Academy of Sciences, 1985,33:693～696
[26] 胡涛．粗糙集理论在不确定性知识表达及故障诊断中的应用研究：[博士学位论文]．电子科技大学，2001
[27] 黄石柱．基于MATLAB的电力机车数字仿真模型．电力系统自动化，2001，4
[28] 刘松柏．SS8电力机车主变流器故障智能诊断系统的研究：[硕士学位论文]．中南大学，2003
[29] 严树刚．SS4系列电力机车变流装置故障分析与对策．机车电传动，1998，4
[30] 韩力群．人工神经网络理论、设计及应用．北京：化学工业出版社，2002，1
[31] 袁小宏：机械诊断中的信息融合技术，西安交通大学博士学位论文，1998．5
[32] 徐光华：概率神经网络及其大机组智能诊断技术，西安交通大学博士学位
论文1995．10
[33] 高文：KDD数据库中的知识发现，计算机世界报，1998年9月28日D1
[34] 朱廷劭，王军：数据挖掘应用，计算机世界报，1998年9月28日D5
[35] 曾黄麟：粗集理论及其应用．重庆大学出版社，1996
[36] 胡昌华，张军波：基于MATLAB的系统分析与设计——小波分析．西安：西安电子科技大学出版社，1995．2
[37] 崔光玮．SS8机车变流装置中硅元件损坏后的故障现象．机车电传动，2002，
[38] 潘新民，王燕芳．微型计算机与传感器技术．北京：人民邮电出版社，1988
[39] 徐德鸿，马皓．电力电子装置故障自动诊断．北京：科学出版社，2001
[40] 刘胜利．测试转台故障诊断专家系统研究与实现：[博士学位论文]．哈尔滨工业大学，2002
[41] 韩道范，李德昌．波形识别在层位对比中的应用．长春科技大学学报，1999，29(4)：397～400
[42] 刘艳斌，罗才双．时域波形的理解与实现．福州大学学报(自然科学版)，1996，24(3)：31～37
[43] 张定会．基于小波分析的故障诊断．上海理工大学学报，2000，22(2)：137～140
[44] 孙颖楷．内燃机智能故障诊断系统的研究及应用：[博士学位论文]．重庆大学，2001
[45] 安利平．粗糙集理论的方法与应用研究：[博士学位论文]．天津大学，2001
[46] 潘丹．基于粗糙集理论的混合智能知识处理研究：[博士学位论文]．华南理工大学，2001
[47] 沈力翔：故障诊断中的不确定信息分析，西安交通大学硕士学位论文，1998
[48] 史东锋：大型回转机械的全息诊断技术研究，西安交通大学博士学位论文，9-24，71-106，1998
[49] 史忠植．知识发现．北京：清华大学出版社，2002
[50] 范明，孟小峰等译．数据挖掘概念与技术．机械工业出版社，2001
[51] 王军．数据库知识发现的研究[D]：[博士学位论文]．北京：中国科学院计算所，1997
[52] 程正兴．小波分析算法与应用．西安：西安交通大学出版社，1998
[53] 辜艺．MATLAB在整流电路中的应用．江汉大学学报(自然科学版)，2002，19(1)：59～61
[54] 施鸿宝，王秋荷．专家系统．西安：西安交通大学出版社，1991
[2] F. Mitchell,D.H.Sleeman, R. Milne:KA the KDD way or How to doKnowledge Acquisition without completely annoying your expert, 1999.
[3] R. Nowicki et al.: Rough Sets Analysis of Diagnostic Capacity of Viberoacoustic Symptoms, Computer Math. Applic., Vol.24,No.7,109-123,1992
[4] W. Ziarko: Variable Precision Rough Model, Joural of Computer and System 46, 39-59, 1993
[5] Z. Pawlak: Rough Sets, International J. of Computer and Sciences, Vol.11,No.5, 341-356, 1982
[6] Z. Pawlak: Rough Sets: Theoretical Aspects of Reasoning About Data. Dordrecht: Kluwer, 1991
[7] Z. Pawlak, J. Grzymala-Busse, R. Slowinski, W. Ziarko: Rough Sets, Communications of the ACM, Vol.38, No. 11, 89-95, November 1995
[8] http://www, idi.ntnu.no/～aleks/rosetta/
[9] S. Letourneau: Discovering Useful Knowledge from Aircraft Operation/Maintenance Data, Workshop on Machine Learning Applications in the Real World: Methodological Aspects and Implications. ICML97, 1997.
[10] M. Lehane, F. Dube, M. Halasz, R. Orchard, R. Wylie, M. Zaluski: Integrated Diagnostic System (IDS) for Aircraft Fleet Maintenace, http://ai.iit.nrc.ca/IR_public, 1998
[11] M. Halasz, F. Dube B. Orchard: The Integrated Diagnostic System(IDS): Remote Monitoring and Decision Support for Commercial Aircraft-Putting Theory into Practice, 1999
[12] The KATE Softwre Suite, www.acknosoft.com/ftools.html, 1999,10
[13] Nowicki R. Slowinski R. Jermy E,Evaluation of vibroacoustic diagnostic sy mptoms by means of the rough sets theory [J] Computers in Industry. 1997. 20:141—152
[14] Pawlak Z Rough sets [J] International Journal of Information and Computer Science. 2000. 11 (5): 341—348
[15] Pawlak Z,Reasoning about data --A rough set prespective,LNAI 1424,Proceeding of RSCTC 98 [C],Warsaw;Springer, 1998
[16] Pawlak Z. Busse J G. Slowinski R. et al Rough sets [J] Communcations Of the ACM, 1999, 38 (11): 89～95
[17] Johnson S D. Approximation algorithms for combinatorial problems. Journal of Computer and System Sciences, 1974(9):256-278
[18] Andrzej Skowron, Hung Son Nguyen, Bool reasoning scheme with some application in data mining. PKDD, 1999
[19] Holland, J. H., Genetic algorithms and the optimal allocations of trials, SIAM Journal of Computing, 2,88-105,1973
[20] hrn A. Discernibility and Rough Sets in Medicine: Tools and Applications: [PhD thesis]. Trondheim, Norway: Norwegian university of science and Technology, Department of Computer and Information Science, 1999
[21] Bhavani Thuraisingham, A primer for understanding and applying data mining [J],IT Professional,2000,2(1):28-31.
[22] Christ M. Atkinson. etal, Virtual Sensing: A Neural Network Based On Intelligent Performance and Emission Prediction System for On Board Diagnose and Engine Control SAE 980516
[23] Cristina Olaru, Louis Wehenkel. Data mining [J].IEEE Computer Applications in Power,1999,12(1): 19-25.
[24] Fayyad U, Piatetsky- Shapiro G, Smyth R, From Data mining to Knowledge Discovery; An Overview, In; Faygad U, ed, Advances in Knowledge Discovery and Data Mining[M],AAAI Press/The MT Press, 1996,1-34.
[25] Wong S K M, Ziarko W. On optimal decision rules in decision tables. Bulletin of Polish Academy of Sciences, 1985,33:693～696
[26] 胡涛．粗糙集理论在不确定性知识表达及故障诊断中的应用研究：[博士学位论文]．电子科技大学，2001
[27] 黄石柱．基于MATLAB的电力机车数字仿真模型．电力系统自动化，2001，4
[28] 刘松柏．SS8电力机车主变流器故障智能诊断系统的研究：[硕士学位论文]．中南大学，2003
[29] 严树刚．SS4系列电力机车变流装置故障分析与对策．机车电传动，1998，4
[30] 韩力群．人工神经网络理论、设计及应用．北京：化学工业出版社，2002，1
[31] 袁小宏：机械诊断中的信息融合技术，西安交通大学博士学位论文，1998．5
[32] 徐光华：概率神经网络及其大机组智能诊断技术，西安交通大学博士学位
论文1995．10
[33] 高文：KDD数据库中的知识发现，计算机世界报，1998年9月28日D1
[34] 朱廷劭，王军：数据挖掘应用，计算机世界报，1998年9月28日D5
[35] 曾黄麟：粗集理论及其应用．重庆大学出版社，1996
[36] 胡昌华，张军波：基于MATLAB的系统分析与设计——小波分析．西安：西安电子科技大学出版社，1995．2
[37] 崔光玮．SS8机车变流装置中硅元件损坏后的故障现象．机车电传动，2002，
[38] 潘新民，王燕芳．微型计算机与传感器技术．北京：人民邮电出版社，1988
[39] 徐德鸿，马皓．电力电子装置故障自动诊断．北京：科学出版社，2001
[40] 刘胜利．测试转台故障诊断专家系统研究与实现：[博士学位论文]．哈尔滨工业大学，2002
[41] 韩道范，李德昌．波形识别在层位对比中的应用．长春科技大学学报，1999，29(4)：397～400
[42] 刘艳斌，罗才双．时域波形的理解与实现．福州大学学报(自然科学版)，1996，24(3)：31～37
[43] 张定会．基于小波分析的故障诊断．上海理工大学学报，2000，22(2)：137～140
[44] 孙颖楷．内燃机智能故障诊断系统的研究及应用：[博士学位论文]．重庆大学，2001
[45] 安利平．粗糙集理论的方法与应用研究：[博士学位论文]．天津大学，2001
[46] 潘丹．基于粗糙集理论的混合智能知识处理研究：[博士学位论文]．华南理工大学，2001
[47] 沈力翔：故障诊断中的不确定信息分析，西安交通大学硕士学位论文，1998
[48] 史东锋：大型回转机械的全息诊断技术研究，西安交通大学博士学位论文，9-24，71-106，1998
[49] 史忠植．知识发现．北京：清华大学出版社，2002
[50] 范明，孟小峰等译．数据挖掘概念与技术．机械工业出版社，2001
[51] 王军．数据库知识发现的研究[D]：[博士学位论文]．北京：中国科学院计算所，1997
[52] 程正兴．小波分析算法与应用．西安：西安交通大学出版社，1998
[53] 辜艺．MATLAB在整流电路中的应用．江汉大学学报(自然科学版)，2002，19(1)：59～61
[54] 施鸿宝，王秋荷．专家系统．西安：西安交通大学出版社，1991