基于二进制粒子群优化的卫星典型件工艺知识挖掘

doi:10.12068/j.issn.1005-3026.2015.01.026

东北大学学报:自然科学版 ›› 2015, Vol. 36 ›› Issue (1): 119-123.DOI: 10.12068/j.issn.1005-3026.2015.01.026

基于二进制粒子群优化的卫星典型件工艺知识挖掘

王琳¹，张永健²，钟诗胜¹，刘金山³

(1.哈尔滨工业大学机电工程学院，黑龙江哈尔滨150001;2.哈尔滨工业大学(威海) 船舶与海洋工程学院，山东威海264209;3.中国空间技术研究院北京卫星制造厂，北京100094)

收稿日期:2013-10-01 修回日期:2013-10-01 出版日期:2015-01-15 发布日期:2014-11-07
通讯作者: 王琳
作者简介:王琳(1986-)，男，山东济阳人，哈尔滨工业大学博士研究生; 钟诗胜(1964-)，男，江西龙南人，哈尔滨工业大学教授，博士生导师.
基金资助:
国家自然科学基金资助项目(51075083); 高等学校博士学科点专项科研基金资助项目(20112302130003).

Binary Particle Swarm Optimization Based Process Knowledge Mining for Typical Parts of Satellite

WANG Lin¹， ZHANG Yong-jian²， ZHONG Shi-sheng¹， LIU Jin-shan³

1. School of Mechanical & Electrical Engineering， Harbin Institute of Technology， Harbin 150001， China; 2. School of Naval Architecture and Ocean Engineering， Harbin Institute of Technology at Weihai， Weihai 264209， China; 3. Beijing Satellite Manufacturing Factory， China Academy of Space Technology， Beijing 100094， China.

Received:2013-10-01 Revised:2013-10-01 Online:2015-01-15 Published:2014-11-07
Contact: ZHANG Yong-jian
About author:-
Supported by:
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摘要/Abstract

摘要： 针对卫星典型件在工艺设计过程中设计任务量大、重复性工作多，且其历史工艺数据未能充分有效利用的问题，进行了工艺知识挖掘的研究，以提高工艺知识的重用性.首先对工艺知识挖掘问题进行了描述，建立了工艺知识的关联规则模型;然后针对海量数据中Apriori算法挖掘效率低的问题引入二进制粒子群优化(BPSO)算法，并构造了基于BPSO的关联规则挖掘算法.最后对卫星结构板这一典型件的历史工艺数据进行挖掘，得到了卫星结构板典型工序序列.基于BPSO的关联规则挖掘算法可以有效提高工艺知识的挖掘效率.

关键词: 粒子群优化, 典型件, 工艺知识, 关联规则挖掘, 典型工序序列

Abstract: The huge quantity of design of manufacturing process of satellite typical parts and a lot of repeated jobs existed in the process. Many kinds of process knowledge without reused effectively contained in the historical process data. Process knowledge mining algorithm was studied in order to increase efficiency. The problem was described firstly， and the association rule model was built. In order to improve computational efficiency of Apriori algorithm for huge datasets， binary particle swarm optimization(BPSO) was introduced. Meanwhile association rule mining algorithm based on BPSO was designed. Finally， the designed algorithm was used in process knowledge mining for satellite plate. The mining efficiency of process knowledge can be improved effectively by the association rule mining algorithm based on BPSO.

Key words: particle swarm optimization, typical part, process knowledge, association rule mining, typical operation sequence

中图分类号:

TP391

王琳，张永健，钟诗胜，刘金山. 基于二进制粒子群优化的卫星典型件工艺知识挖掘[J]. 东北大学学报:自然科学版, 2015, 36(1): 119-123.

WANG Lin， ZHANG Yong-jian， ZHONG Shi-sheng， LIU Jin-shan. Binary Particle Swarm Optimization Based Process Knowledge Mining for Typical Parts of Satellite[J]. Journal of Northeastern University Natural Science, 2015, 36(1): 119-123.

参考文献

[1]Gülser K，nci B，Murat T.A review of data mining applications for quality improvement in manufacturing industry［J］.Expert Systems with Applications，2011，38(1):13448-13467.
[2]Moon S K，Simpson T W，Kumara S R T.A methodology for knowledge discovery to support product family design［J］.Annals of Operations Research，2010，174(1):201-218.
[3]Zhang L L，Jiao R J.Identifying mapping relationships between functions and technologies:an approach based on association rule mining［C］// International Conference on Industrial Engineering and Engineering Management(IEEM).Singapore:IEEE，2011:1596-1601.
[4]Kamsu-Foguem B，Rigal F，Mauget F.Mining association rules for the quality improvement of the production process［J］.Expert Systems with Applications，2013，40(4):1034-1045.
[5]高伟，殷国富，成尔京.机械制造工艺序列中的知识发现方法研究［J］.机械工程学报，2004，40(5):121-125，130.(Gao Wei，Yin Guo-fu，Cheng Er-jing.Research on method of knowledge discovery in manufacturing process sequence［J］.Chinese Journal of Mechanical Engineering，2004，40(5):121-125，130.)
[6]Li C R，Yang C X，Xiong P.Implementation of the multi-agent-based product knowledge extraction system［C］// WRI Global Congress on Intelligent Systems.Xiamen，2009:548-552.
[7]Sarath K N V D，Ravi V.Association rule mining using binary particle swarm optimization［J］.Engineering Applications of Artificial Intelligence，2013，26(8):1832-1840.
[8]Patel B，Chaudhary V K，Karan R K，et al.Optimization of association rule mining Apriori algorithm using ACO［J］.International Journal of Soft Computing and Engineering，2011，1(1):24-26.
[9]Soni R K，Gupta N，Sinhal A.An FP-growth approach to mining association rules［J］.International Journal of Computer Science and Mobile Computing，2013，2(2):1-5.
[10]黄嘉满，张冬茉.基于文本的关联规则提取方法的研究［J］.计算机仿真，2008，25(1):96-99.(Huang Jia-man，Zhang Dong-mo.An algorithm for mining association rule in textual database［J］.Computer Simulation，2008，25(1):96-99.)
[11]Indira K，Kanmani S，Prashanth P，et al.Population based search methods in mining association rules［C］// The Third International Conference，CNC 2012.Chennai，2012:255-261.一位数()
[10]二位数()

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基于二进制粒子群优化的卫星典型件工艺知识挖掘

Binary Particle Swarm Optimization Based Process Knowledge Mining for Typical Parts of Satellite

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