数控机床维护服务过程中的知识重用
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摘要
从产品生命周期角度,数控机床既是设备又是产品,其可靠性的优劣不仅影响所加工零件质量的好坏,还影响设备供应商的利益与口碑,而有效的维护服务在提高设备可靠性、改善生产效率方面具有非常重要的作用。重用历史维护服务知识是解决新故障问题和改善维护服务的有效手段。以案例推理过程作为知识重用依据,采用两层本体的知识组织方式,建立了设备维护服务领域本体,通过使用基于语义相似度和相关度的适应案例检索,在设定的选择阈值下,选取适应度最高的相似历史案例,并采用基于因果理论和依赖关系的适应算法,得到当前问题的解决方案。以型号为CH7520C的数控加工中心为例,验证了所提方法的有效性。
From product lifecycle point of view,the high value and sophisticated Computer Numerical Control(CNC)machine tools are both equipment and products,whose reliability affects not only the quality of manufactured parts,but also the profits of equipment suppliers.Effective maintenance and services play an important role in increasing equipment's reliability and improving manufacturing efficiency.Reusing historical knowledge is an effective way to solve new problems and improve maintenance and service efficiency.According to Case-based Reasoning(CBR),two-layer ontology was adopted to represent field knowledge by using Adaptation-guided Retrieval(AGR).Based on semantic similarity and correlation,the most adaptive historical case was selected under the default threshold,and the adaption algorithm was illustrated based on casual theory and the dependence relationship to generate the solution for current new problem.The proposed method was illustrated and verified by using the type of CH7520 Cof CNC machine tool as an example.
From product lifecycle point of view,the high value and sophisticated Computer Numerical Control(CNC)machine tools are both equipment and products,whose reliability affects not only the quality of manufactured parts,but also the profits of equipment suppliers.Effective maintenance and services play an important role in increasing equipment's reliability and improving manufacturing efficiency.Reusing historical knowledge is an effective way to solve new problems and improve maintenance and service efficiency.According to Case-based Reasoning(CBR),two-layer ontology was adopted to represent field knowledge by using Adaptation-guided Retrieval(AGR).Based on semantic similarity and correlation,the most adaptive historical case was selected under the default threshold,and the adaption algorithm was illustrated based on casual theory and the dependence relationship to generate the solution for current new problem.The proposed method was illustrated and verified by using the type of CH7520 Cof CNC machine tool as an example.
引文
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[25] ZHOU Wei,ZHOU Chuangming,SHI Chaohui,et al.Rough sets theory and application[M].1st ed.Beijing:Tsinghua University Press,2015(in Chinese).[周炜,周创明,史朝辉,等.粗糙集理论及应用[M].1版.北京:清华大学出版社,2015.]
[26] YANG Binyu.Application of case-based reasoning in intelligent decision support system[D].Changchun:Jilin University,2004(in Chinese).[杨斌宇.基于案例的推理在智能决策支持系统中的应用[D].长春:吉林大学,2004.]
[2] YANG Dongsheng,LI Hongwei,SUN Yilan,et al.Study on novel grey relation analysis method and its application in fault recognition of NC machine tool spindle[J].Chinese Mechanical Engineering,2013,24(23):3150-3153(in Chinese).[杨东升,李红卫,孙一兰,等.新型灰关联分析方法研究及其在数控机床主轴故障识别中的应用[J].中国机械工程,2013,24(23):3150-3153.]
[3] GAO Min,LIU Zhongjun.The discussion of BNN-002CNC machining center spindle component maintenance process[J].Machine Tool&Hydraulics,2014,42(20):149-150(in Chinese).[高敏,刘忠军.BNN-002加工中心主轴部件维修工艺探讨[J].机床与液压,2014,42(20):149-150.]
[4] CHEN Weiwei,ZHANG Hua,XIANG Qin,et al.Research on CNC machine tool cutting energy consumption prediction based on support vector machine[J].Machinery Design&Manufacture,2014,(11):54-56(in Chinese).[陈薇薇,张华,向琴,等.基于支持向量机数控机床切削能耗预测方法研究[J].机械设计与制造,2014,(11):54-56.]
[5] SONG Junyou.Research on configuration design of product service system for CNC machine tools[D].Shenyang:Northeastern University,2014(in Chinese).[宋均有.数控机床产品服务系统配置设计研究[D].沈阳:东北大学,2014.]
[6] ZHU H,GAO J,CAI Q.A product-service system using requirement analysis and knowledge management technologies[J].Kybernetes,2015,44(5):823-842.
[7] LI Congdong,XIE Tian,TANG Yongli.Knowledge expression and service on demand process reasoning framework of manufacturing system based on SWRL[J].Computer Integrated Manufacturing Systems,2013,19(1):187-198(in Chinese).[李从东,谢天,汤勇力.基于SWRL的制造系统知识表达与按需服务过程推理框架[J].计算机集成制造系统,2013,19(1):187-198.]
[8] WU Shanyan.Research on domain ontology-based CBR optimization and its application[D].Shanghai:East China Normal University,2014(in Chinese).[吴珊燕.基于领域本体的案例推理优化及其应用研究[D].上海:华东师范大学,2014.]
[9] BATET M,SNCHEZ D,VALLS A.An ontology-based measure to compute semantic similarity in biomedicine[J].Journal of Biomedical Informatics,2011,44(1):118-125.
[10] ZHANG Wei,CAO Fasheng,YU Quan,et al.Research on the least common subsumer in description logicεLN[J].Computer Engineering&Science,2012,34(2):139-145(in Chinese).[张维,曹发生,余泉,等.描述逻辑系统εLN中概念最小公共包含推理研究[J].计算机工程与科学,2012,34(2):139-145.]
[11] ZANG F.Research on semantic distance calculation method of ontology concept[J].Computer&Network,2016(10):73-75(in Chinese).[臧芳.本体概念间语义距离计算方法探索[J].计算机与网络,2016(10):73-75.]
[12] HAOUCHINE K,CHEBEL-MORELLO B,ZERHOUNI N.Adaptation-Guided retrieval for a diagnostic and repair help system dedicated to a pallets transfer[EB/OL].[2017-03-25].http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=056CDA960CEB8FE99FB6FF8B1B25E78A?doi=10.1.1.371.2411&rep=rep1&type=pdf.
[13] SMYTH B,KEANE M T.Adaptation-guided retrieval:questioning the similarity assumption in reasoning[J].Artificial Intelligence,1998,102(2):249-293.
[14] POTES R P A,KAMSU F B,NOYES D.Knowledge reuse integrating the collaboration from experts in industrial maintenance management[J].Knowledge-Based Systems,2013,50(3):171-186.
[15] HUANG Hongyu.Research on method of emergency case representation and retrieval based on knowledge element[D].Dalian:Dalian University of Technology,2013(in Chinese).[黄红雨.基于知识元的应急案例表示及检索方法研究[D].大连:大连理工大学,2013.]
[16] GAO Junjie.The key technology of semantic web-based enterprise knowledge integration[D].Dalian:Dalian University of Technology,2009(in Chinese).[高俊杰.基于语义Web的企业知识集成关键技术研究[D].大连:大连理工大学,2009.]
[17] WAN S,LI D,GAO J,et al.A collaborative machine tool maintenance planning system based on content management technologies[J].The International Journal of Advanced Manufacturing Technology,2016,94(5/6/7/8):1639-1653.
[18] GAN Jianhou,JIANG Yue,XIA Youming.Ontology and its application[M].1st ed.Beijing:Science Press,2011(in Chinese).[甘健侯,姜跃,夏幼明.本体方法及其应用[M].第1版.北京:科学出版社,2011.]
[19] JABROUNI H,KAMSU F B,GENESTE L.StructuralModel Approach of Causal reasoning in problem solving processes[C]//Proceedings of the 2011IEEE International Conference on Information Reuse&Integration.Washington,D.C.,USA:IEEE,2011:271-274.
[20] HU Zhe,ZHENG Cheng.An improved algorithm of concept similarity computation based on domain ontology[J].Journal of Qiqihar University:Natural Science,2013,29(1):1-6(in Chinese).[胡哲,郑诚.一种改进的基于领域本体的概念语义相似度算法[J].齐齐哈尔大学学报:自然科学版,2013,29(1):1-6.]
[21] BAO Xinzhong,LIU Cheng.A new method of ascertaining attribute weight based on rough set theory[J].Chinese Journal of Management,2009,6(6):729-732(in Chinese).[鲍新中,刘澄.一种基于粗糙集的权重确定方法[J].管理学报.2009,6(6):729-732.]
[22] LIU Yu,BEN Kerong.Weight coefficient determination for case feature based on attribute importance[J].Computer Integrated Manufacturing Systems,2012,18(6):1230-1235(in Chinese).[柳玉,贲可荣.基于属性重要度的案例特征权重确定方法[J].计算机集成制造系统.2012,18(6):1230-1235.]
[23] ZHANG Xiaonan.Case-based mobile phone failure similarity matching algorithm[D].Qingdao:Ocean University of China,2014(in Chinese).[张肖南.基于案例的手机故障相似度匹配算法研究[D].青岛:中国海洋大学,2014.]
[24] CAO Xiuying,LIANG Jingguo.The Method of ascertaining attribute weight based on rough sets theory[J].Chinese Journal of Management Science.2002,10(5):98-100(in Chinese).[曹秀英,梁静国.基于粗集理论的属性权重确定方法[J].中国管理科学.2002,10(5):98-100.]
[25] ZHOU Wei,ZHOU Chuangming,SHI Chaohui,et al.Rough sets theory and application[M].1st ed.Beijing:Tsinghua University Press,2015(in Chinese).[周炜,周创明,史朝辉,等.粗糙集理论及应用[M].1版.北京:清华大学出版社,2015.]
[26] YANG Binyu.Application of case-based reasoning in intelligent decision support system[D].Changchun:Jilin University,2004(in Chinese).[杨斌宇.基于案例的推理在智能决策支持系统中的应用[D].长春:吉林大学,2004.]