机械故障智能诊断系统本体建模及推理的应用研究
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摘要
近年来,机械故障智能诊断系统的研究成为机械故障诊断领域的研究热点,而系统的智能化程度和诊断准确度依赖于系统知识库中知识的数量与质量以及知识的组织、分类以及更进一步的知识共享和推理。本体作为一种能在语义和知识层次上描述知识模型的建模工具,可以提供对某一领域的概念以及相互关系的概念化描述,为知识共享奠定基础,本体支持对知识信息的区分,可以实现对领域知识的层次化表示,本体中包含的类公理以及约束公理可以用于知识的推理,且现有的本体推理机可以对本体提供直接的推理服务。
本文主要研究如何应用本体构建机械故障智能诊断系统的知识库,使用本体推理保证知识库知识的正确性以及通过本体推理实现故障诊断的方法。具体对以下内容进行了分析和研究:
(1)针对机械故障诊断需求,在现有的领域本体建模方法的基础上提出了一套适用于机械故障诊断领域的本体构建方法,通过对故障领域知识的分析,提出了纵向建模方案,对故障诊断领域知识根据故障类型、故障征兆、故障原因以及故障处理方法进行分类。通过定义属性来表达故障类型、故障征兆、故障原因以及故障处理方法之间存在的复杂的对应关系,从而将故障诊断领域知识组织为具备语义关系的本体知识库。
(2)依据提出的机械故障诊断领域的本体构建方法以及纵向建模方案,以一类交流电动机故障为例,使用本体建模工具Protégé4.0构建了交流电动机故障诊断本体,验证了本文提出的方法的可行性。
(3)本文提出通过定义复合类的方法实现故障诊断,即使用OWL中的构造子对故障类型做复合类定义并将其声明为等价类,本体推理机通过对等价类的推理实现故障诊断。
(4)使用本体推理机对故障诊断本体进行了推理,对于推理检测出的逻辑错误提出了相应的解决方案,并通过对等价类的推理,实现了对起动故障、过热故障以及电刷等故障的诊断,其诊断结果的准确性和合理性较高。
结果表明,本文提出的机械故障诊断领域的本体构建及推理的方法具有可行性,为机械故障智能诊断领域的本体建模及推理的理论与方法研究提供了一条值得探索的途径。
In recent years, the study of intelligent mechanical fault diagnosis has become a research focus in the field of mechanical fault diagnosis. The intelligent level and diagnostic accuracy of system depend on the quantity and quality of knowledge in knowledge base, knowledge organization and classification, and further knowledge sharing and reasoning as well. Ontology can describe knowledge model on semantic and knowledge level, and it also can provide conceptualization description on conceptions and relationship in a certain field, thereby making a foundation for knowledge sharing. Additionally, ontology supports distinction of knowledge information achieving level representation of domain knowledge. Class axioms and constraint axioms in ontology can be used to knowledge reasoning, and the existing ontology reasoner can provide a direct reasoning service for ontology.
This thesis mainly study on how to build a knowledge base of intelligent mechanical fault diagnosis system by using ontology, including ontology reasoning to ensure the correctness of knowledge of knowledge base and achieve fault diagnosis. Overall, this thesis makes the following contributions:
(1) Aiming at the demand of mechanical fault diagnosis, based on the existing ontology modeling method, this thesis proposes an ontology modeling method to apply to the field of mechanical fault diagnosis. Furthermore, a longitudinal modeling is proposed by analyzing mechanical fault diagnosis knowledge. According to the fault type, fault symptom, fault reasons and fault disposals, fault diagnosis knowledge is classified. By defining property to express the complex relationship among fault type, fault symptom, fault reasons and disposals, mechanical fault diagnosis knowledge becomes knowledge base with semantic relation.
(2) According to the ontology modeling method and longitudinal modeling proposed in our thesis, an ontology of AC motor fault diagnosis is constructed by using Protégé4.0, and the feasibility of our method is verified.
(3) A method of implementing fault diagnosis is proposed. Specifically, using constructor of OWL to define complex classes which is further declared as equivalent classes, the implementation fault diagnosis is achieved by reasoning equivalent classes.
(4) Aiming at the logic error detected by ontology reasoner, the corresponding solutions are proposed. By ontology reasoning, fault diagnosises including starting fault, overheating fault and brush fault are achieved and the accuracy and rationality of the result are comparatively high.
The result of our experiment shows the feasibility of the ontology modeling and reasoning method proposed in our thesis, which offers a new way for ontology modeling and reasoning research in the field of intelligent mechanical fault diagnosis.
本文主要研究如何应用本体构建机械故障智能诊断系统的知识库,使用本体推理保证知识库知识的正确性以及通过本体推理实现故障诊断的方法。具体对以下内容进行了分析和研究:
(1)针对机械故障诊断需求,在现有的领域本体建模方法的基础上提出了一套适用于机械故障诊断领域的本体构建方法,通过对故障领域知识的分析,提出了纵向建模方案,对故障诊断领域知识根据故障类型、故障征兆、故障原因以及故障处理方法进行分类。通过定义属性来表达故障类型、故障征兆、故障原因以及故障处理方法之间存在的复杂的对应关系,从而将故障诊断领域知识组织为具备语义关系的本体知识库。
(2)依据提出的机械故障诊断领域的本体构建方法以及纵向建模方案,以一类交流电动机故障为例,使用本体建模工具Protégé4.0构建了交流电动机故障诊断本体,验证了本文提出的方法的可行性。
(3)本文提出通过定义复合类的方法实现故障诊断,即使用OWL中的构造子对故障类型做复合类定义并将其声明为等价类,本体推理机通过对等价类的推理实现故障诊断。
(4)使用本体推理机对故障诊断本体进行了推理,对于推理检测出的逻辑错误提出了相应的解决方案,并通过对等价类的推理,实现了对起动故障、过热故障以及电刷等故障的诊断,其诊断结果的准确性和合理性较高。
结果表明,本文提出的机械故障诊断领域的本体构建及推理的方法具有可行性,为机械故障智能诊断领域的本体建模及推理的理论与方法研究提供了一条值得探索的途径。
In recent years, the study of intelligent mechanical fault diagnosis has become a research focus in the field of mechanical fault diagnosis. The intelligent level and diagnostic accuracy of system depend on the quantity and quality of knowledge in knowledge base, knowledge organization and classification, and further knowledge sharing and reasoning as well. Ontology can describe knowledge model on semantic and knowledge level, and it also can provide conceptualization description on conceptions and relationship in a certain field, thereby making a foundation for knowledge sharing. Additionally, ontology supports distinction of knowledge information achieving level representation of domain knowledge. Class axioms and constraint axioms in ontology can be used to knowledge reasoning, and the existing ontology reasoner can provide a direct reasoning service for ontology.
This thesis mainly study on how to build a knowledge base of intelligent mechanical fault diagnosis system by using ontology, including ontology reasoning to ensure the correctness of knowledge of knowledge base and achieve fault diagnosis. Overall, this thesis makes the following contributions:
(1) Aiming at the demand of mechanical fault diagnosis, based on the existing ontology modeling method, this thesis proposes an ontology modeling method to apply to the field of mechanical fault diagnosis. Furthermore, a longitudinal modeling is proposed by analyzing mechanical fault diagnosis knowledge. According to the fault type, fault symptom, fault reasons and fault disposals, fault diagnosis knowledge is classified. By defining property to express the complex relationship among fault type, fault symptom, fault reasons and disposals, mechanical fault diagnosis knowledge becomes knowledge base with semantic relation.
(2) According to the ontology modeling method and longitudinal modeling proposed in our thesis, an ontology of AC motor fault diagnosis is constructed by using Protégé4.0, and the feasibility of our method is verified.
(3) A method of implementing fault diagnosis is proposed. Specifically, using constructor of OWL to define complex classes which is further declared as equivalent classes, the implementation fault diagnosis is achieved by reasoning equivalent classes.
(4) Aiming at the logic error detected by ontology reasoner, the corresponding solutions are proposed. By ontology reasoning, fault diagnosises including starting fault, overheating fault and brush fault are achieved and the accuracy and rationality of the result are comparatively high.
The result of our experiment shows the feasibility of the ontology modeling and reasoning method proposed in our thesis, which offers a new way for ontology modeling and reasoning research in the field of intelligent mechanical fault diagnosis.
引文
[1]杨昌昊.基于不确定性理论的机械故障智能诊断方法研究[D].合肥:中国科学技术大学. 2008.11.
[2] M.Uschold, M.Gruninger. Priuciples, methods and applications [J]. Knowledge Engineering Review. 1996, 11(2): 5-10.
[3] L.A.Zedeh. The role of fuzzy logic in the management of uncertainty in expert systems [J]. Fuzzy Sets and Systems. 1983, (11): 199-227.
[4] P.K.Fink, J.C.Lusth. Expert system and diagnostic expertise in the mechanical and electrical domains [J]. IEEE Trans. On SMC. 1997, 17(3): 340-349.
[5]邓志鸿,唐世渭. Ontology研究综述[J].北京大学学报(自然科学版). 2002, (5): 730-738.
[6]王进鹏,张亚非,杨壮.基于本体的关系数据集成的查询处理[J].计算机科学. 2010, 37(2): 134-137.
[7]李广明.基于本体的知识资源模糊聚类分析[J].计算机应用研究. 2011, 28(2): 585-586,590.
[8] Z.Hui, G.Weiying. A reasearch on Qos-based ontology model for web services discovery [C]. Second International Workshop on Knowledge Discovery and Data Mining. China Academic Journal Electronic Publishing House. 2009: 787-789.
[9] A.D.Ostrowski. Rule definition for managing ontology development [J]. Computer Science. 2007: 174-181.
[10] L.ChunNian, H.JunYing. Application of domain ontology-based on semantic web technology [C]. 2009 International Conference on Computational Intelligence and Natural Computing. 2009: 133-136.
[11]邹国兵,向阳.基于领域本体的信息搜索模型[J].同济大学学报(自然科学报). 2009, 37(4): 545-549.
[12]何庆,汤庸,黄永钊.基于本体的法律知识库的研究与实现[J].计算机科学. 2007, 23(2): 22-28.
[13] Y.Yang, J.Calmet. OntoBayes: an ontology-driven uncertainty model [C]. Dharmendra Sharma. Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1. Washington, DC, USA: IEEE Computer Society. 2005: 457-463.
[14] Y.Kitamura, R.Mizoguchi. An ontological analysis of fault process and category of faults [C]. Loch Awe. Proc. of Tenth International Workshop on Principles of Diagnosis (DX-99). Japan: Osaka University. 1999: 118-128.
[15] A.Bernaras, I.Laresgoiti, N.Bartolome, J.Corera. An ontology for fault diagnosis in electrical networks [C]. Proceedings of International Conference on Intelligent Systems Applications to Power Systems. 1996: 199-210.
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[17]时念云,腾良娟等.基于本体知识共享的汽车故障诊断专家系统[J].计算机系统应用. 2006, 18-20.
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[19]牛强,夏士雄,谭国俊等.基于描述逻辑的电机故障诊断知识表示与推理[J].小型微型计算机系统.2009, 30(5): 872~875.
[20] R.Iliffe. Abstract considerations:disciplines and the incoherence of Newton’s natural philosophy [J]. Studies In History and Philosophy of Science Part A. 2004, 35(3): 427~454.
[21] J.M.Kim, H.P.Shin. A methodology for constructing of philosophy ontology based on philosophical texts [J]. Computer Standards & Interfaces. 2007, 28(3): 302~315.
[22] R.Neches, T.Fimin. Enabling technology for knowledge sharing [J]. AIMagazine. 1991, 12(3):36~56.
[23] T.R.Gruber. A Translation approach to portable ontology specifications [J]. Knowledge Acquisition. 1993: 199~220.
[24] A.G.Perez, V.R.Benjamins. Overview of knowledge sharing and reuse components: ontologies and problem-solving methods [C]. V R Stockholm, B Benjamins, A Chandrasekaram. Proceedings of the IJCAI-99 workshop on Ontologies and Problem-Solving Methods (KRR5). Stockholm, Sweden: CEUR-WS. 1999: 1-15.
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[28] V.K.Chaudhri, A.Farquhar, R.Fikes. A programmatic foundation for knowledge base interoperability [C]. the 15th National Conf on Artificial Intelligence (AAA1-98). Madison, Wisconsin: AAA1 Press/MIT Press. 1998.
[29] J.D.Heflin. Towards the semantic web: knowledge representation in adynamic distributed environment [D]. Maryland: University of Maryland. 2001.
[30] D.Briekley, R.V.Guha. RDF vocabulary description language1.0: RDF sehema [EB/OL]. http://www.w3.org/TR/2002/WD-rdf-schema-20021112/. 2010.
[31] F.Manola, E.Miller. RDF primer [EB/OL]. http://www.w3.org/TR/2004/REC-rdf-Primer-20040210/. 2004.
[32] P.Hayes. RDF semanties [EB/OL]. http://www.w3.org/TR/2004/REC-rdf-mt-20040210/#RDFINTERP. 2004.
[33] K.M.Smith, L.D.Mcquinness. OWL web ontology language guide [EB/OL]. . http://www.w3.org/TR/2004/rec-owl-guide-20040210/. 2004.
[34] Protégé. http://protege.stanford.edu/. 2010.
[35]王辉,冯志勇,陈炬.基于本体的服务网络平台及其构建方法[J].计算机应用. 2010, 2170-2172.
[36]雍兮,曹存根,白埃民.基于本体的智能规划方法研究与应用[J].计算机科学. 2011, 38(2): 175-178.
[37]肖洪,余锦凤.数字图书馆领域本体构建研究—以数字参考咨询领域为例[J].大学图书馆学报. 2006, 26(6): 26-29.
[38] M.Gruninger. Designing and evaluating generic ontologies [J]. Proeeedings of ECAI96’s Workshopon Ontologieal Engineering. 2006: 53- 64.
[39] J.Stader. Results of the Enterprise Project [C]. Proeeedings of the 16th Int. Conference of the British Computer Soeiety Speeialist Group on Expert Systems, Cambridge. 1996.
[40] http://www.swi.psy.uva.nl/prieets/NewKACTUS/Reports.html. 2010
[41] F.Mariano. Overview of methodologies for building ontologies [J]. Proceedings of IJCAI199’s: Lessovs Learned and Future Trends. 1999, 1-4.
[42] SENSUS的相关网站: http://www.isi.edu/natural - language/resourees/sensus.html. 2010.
[43] N.F.Noy, L.D.Mcguinness. Ontology development 101: A guide to creating your first ontology. [EB/OL]. http://protege.stanford.edu/publications/ontology_development/ontology101.pdf.
[44]石莲,孙吉贵.描述逻辑综述[J].计算机科学. 2006, 33(1): 194-197.
[45] F.Badder, W.Nutt. Theory, implementtation and applications [M]. London, University Press. 2009: 43-95.
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[2] M.Uschold, M.Gruninger. Priuciples, methods and applications [J]. Knowledge Engineering Review. 1996, 11(2): 5-10.
[3] L.A.Zedeh. The role of fuzzy logic in the management of uncertainty in expert systems [J]. Fuzzy Sets and Systems. 1983, (11): 199-227.
[4] P.K.Fink, J.C.Lusth. Expert system and diagnostic expertise in the mechanical and electrical domains [J]. IEEE Trans. On SMC. 1997, 17(3): 340-349.
[5]邓志鸿,唐世渭. Ontology研究综述[J].北京大学学报(自然科学版). 2002, (5): 730-738.
[6]王进鹏,张亚非,杨壮.基于本体的关系数据集成的查询处理[J].计算机科学. 2010, 37(2): 134-137.
[7]李广明.基于本体的知识资源模糊聚类分析[J].计算机应用研究. 2011, 28(2): 585-586,590.
[8] Z.Hui, G.Weiying. A reasearch on Qos-based ontology model for web services discovery [C]. Second International Workshop on Knowledge Discovery and Data Mining. China Academic Journal Electronic Publishing House. 2009: 787-789.
[9] A.D.Ostrowski. Rule definition for managing ontology development [J]. Computer Science. 2007: 174-181.
[10] L.ChunNian, H.JunYing. Application of domain ontology-based on semantic web technology [C]. 2009 International Conference on Computational Intelligence and Natural Computing. 2009: 133-136.
[11]邹国兵,向阳.基于领域本体的信息搜索模型[J].同济大学学报(自然科学报). 2009, 37(4): 545-549.
[12]何庆,汤庸,黄永钊.基于本体的法律知识库的研究与实现[J].计算机科学. 2007, 23(2): 22-28.
[13] Y.Yang, J.Calmet. OntoBayes: an ontology-driven uncertainty model [C]. Dharmendra Sharma. Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1. Washington, DC, USA: IEEE Computer Society. 2005: 457-463.
[14] Y.Kitamura, R.Mizoguchi. An ontological analysis of fault process and category of faults [C]. Loch Awe. Proc. of Tenth International Workshop on Principles of Diagnosis (DX-99). Japan: Osaka University. 1999: 118-128.
[15] A.Bernaras, I.Laresgoiti, N.Bartolome, J.Corera. An ontology for fault diagnosis in electrical networks [C]. Proceedings of International Conference on Intelligent Systems Applications to Power Systems. 1996: 199-210.
[16]窦丹丹,谢强,丁秋林.基于Ontology的故障诊断方法[J].计算机应用. 2009, 29(20): 574-576.
[17]时念云,腾良娟等.基于本体知识共享的汽车故障诊断专家系统[J].计算机系统应用. 2006, 18-20.
[18]杜磊,易建军等.应用本体构造发动机故障诊断专家系统[J].计算机应用. 2006, 26(3): 216-218.
[19]牛强,夏士雄,谭国俊等.基于描述逻辑的电机故障诊断知识表示与推理[J].小型微型计算机系统.2009, 30(5): 872~875.
[20] R.Iliffe. Abstract considerations:disciplines and the incoherence of Newton’s natural philosophy [J]. Studies In History and Philosophy of Science Part A. 2004, 35(3): 427~454.
[21] J.M.Kim, H.P.Shin. A methodology for constructing of philosophy ontology based on philosophical texts [J]. Computer Standards & Interfaces. 2007, 28(3): 302~315.
[22] R.Neches, T.Fimin. Enabling technology for knowledge sharing [J]. AIMagazine. 1991, 12(3):36~56.
[23] T.R.Gruber. A Translation approach to portable ontology specifications [J]. Knowledge Acquisition. 1993: 199~220.
[24] A.G.Perez, V.R.Benjamins. Overview of knowledge sharing and reuse components: ontologies and problem-solving methods [C]. V R Stockholm, B Benjamins, A Chandrasekaram. Proceedings of the IJCAI-99 workshop on Ontologies and Problem-Solving Methods (KRR5). Stockholm, Sweden: CEUR-WS. 1999: 1-15.
[25] T.R.Gruber. Towards principles for the design of ontologies used for knowledge sharing [J]. International Journal of Human-Computer Studies. 1995(43): 907-928.
[26] M.R.Genesereth, R.E.Fike. Knowledge interchange format version 3.0 reference manual [R]. Stanford University, Tech Rep: 1992.
[27] T.R.Gruber. A mechanism to support portable ontologies [R]. Stanford University, Tech Rep: KSL-91-66, 1992.
[28] V.K.Chaudhri, A.Farquhar, R.Fikes. A programmatic foundation for knowledge base interoperability [C]. the 15th National Conf on Artificial Intelligence (AAA1-98). Madison, Wisconsin: AAA1 Press/MIT Press. 1998.
[29] J.D.Heflin. Towards the semantic web: knowledge representation in adynamic distributed environment [D]. Maryland: University of Maryland. 2001.
[30] D.Briekley, R.V.Guha. RDF vocabulary description language1.0: RDF sehema [EB/OL]. http://www.w3.org/TR/2002/WD-rdf-schema-20021112/. 2010.
[31] F.Manola, E.Miller. RDF primer [EB/OL]. http://www.w3.org/TR/2004/REC-rdf-Primer-20040210/. 2004.
[32] P.Hayes. RDF semanties [EB/OL]. http://www.w3.org/TR/2004/REC-rdf-mt-20040210/#RDFINTERP. 2004.
[33] K.M.Smith, L.D.Mcquinness. OWL web ontology language guide [EB/OL]. . http://www.w3.org/TR/2004/rec-owl-guide-20040210/. 2004.
[34] Protégé. http://protege.stanford.edu/. 2010.
[35]王辉,冯志勇,陈炬.基于本体的服务网络平台及其构建方法[J].计算机应用. 2010, 2170-2172.
[36]雍兮,曹存根,白埃民.基于本体的智能规划方法研究与应用[J].计算机科学. 2011, 38(2): 175-178.
[37]肖洪,余锦凤.数字图书馆领域本体构建研究—以数字参考咨询领域为例[J].大学图书馆学报. 2006, 26(6): 26-29.
[38] M.Gruninger. Designing and evaluating generic ontologies [J]. Proeeedings of ECAI96’s Workshopon Ontologieal Engineering. 2006: 53- 64.
[39] J.Stader. Results of the Enterprise Project [C]. Proeeedings of the 16th Int. Conference of the British Computer Soeiety Speeialist Group on Expert Systems, Cambridge. 1996.
[40] http://www.swi.psy.uva.nl/prieets/NewKACTUS/Reports.html. 2010
[41] F.Mariano. Overview of methodologies for building ontologies [J]. Proceedings of IJCAI199’s: Lessovs Learned and Future Trends. 1999, 1-4.
[42] SENSUS的相关网站: http://www.isi.edu/natural - language/resourees/sensus.html. 2010.
[43] N.F.Noy, L.D.Mcguinness. Ontology development 101: A guide to creating your first ontology. [EB/OL]. http://protege.stanford.edu/publications/ontology_development/ontology101.pdf.
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