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

doi:10.12068/j.issn.1005-3026.2015.01.026

Journal of Northeastern University Natural Science ›› 2015, Vol. 36 ›› Issue (1): 119-123.DOI: 10.12068/j.issn.1005-3026.2015.01.026

• Mechanical Engineering • Previous Articles Next Articles

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:
-

Abstract

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

CLC Number:

TP391

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.

References

[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()

[1]	WEI Lin-yang， GUO Xin， WANG Cun-hai， LI Guo-jun. Inversion of Optical Parameters of Semitransparent Media Based on Quantum Particle Swarm Optimization Algorithm [J]. Journal of Northeastern University(Natural Science), 2023, 44(1): 63-69.
[2]	LIU Shi-qi， SONG Jin-chun， YANG Bin. Comparison of Three Stage-Wise Superstructures of Water Network Based on Particle Swarm Optimization [J]. Journal of Northeastern University(Natural Science), 2021, 42(6): 829-834.
[3]	LUO Zhong， XU Di， LI Lei， MA Hui. Bearing Fault Detection Based on Improved CYCBD Method [J]. Journal of Northeastern University(Natural Science), 2021, 42(5): 673-678.
[4]	YANG Dan， LU Tian， GUO Wen-xin， WANG Xu. MIT Image Reconstruction Method Based on Simulated Annealing Particle Swarm Algorithm [J]. Journal of Northeastern University(Natural Science), 2021, 42(4): 531-537.
[5]	LI Yuan-hui ，CHEN Zhi-yang， XU Shi-da. Influence of Wave Velocity Range on Accuracy of PSO Location Algorithm [J]. Journal of Northeastern University(Natural Science), 2021, 42(4): 561-566.
[6]	WANG Xin-gang， WANG Chao， HAN Kai-zhong. Early Fault Diagnosis Method of Rolling Bearings Based on Optimization of VMD and MCKD [J]. Journal of Northeastern University(Natural Science), 2021, 42(3): 373-381.
[7]	TANG Chuan-yin， ZHAO Yi-feng， ZHAO Ya-feng， ZHOU Shu-wen. Research on the Trajectory Tracking Control Method of Intelligent Vehicles [J]. Journal of Northeastern University Natural Science, 2020, 41(9): 1297-1303.
[8]	GONG Ya-dong， ZHANG Wei-jian， CAI Ming， ZHOU Xian-xin. Experimental Study on the Grinding Metamorphic Layer of Nickel-based Single Crystal Superalloy [J]. Journal of Northeastern University Natural Science, 2020, 41(6): 846-851.
[9]	KONG Zhi， LI Shi-cheng， ZHAO Jie. Improved Coral Reef Algorithm [J]. Journal of Northeastern University Natural Science, 2020, 41(2): 163-169.
[10]	NIU Da-peng， ZANG Ya-li， JIA Ming-xing. Optimal Setting for Hydrometallurgical Whole Process Based on Case-Based Reasoning [J]. Journal of Northeastern University Natural Science, 2020, 41(1): 1-6.
[11]	ZHANG Li-jie ， LIU Jian-chang ， TAN Shu-bin. Multi-exit Evacuation Strategy Based on Particle Swarm Optimization with Background Field [J]. Journal of Northeastern University Natural Science, 2019, 40(9): 1222-1227.
[12]	LI Bai-chun， WANG Zhen-yu， ZHANG Bin， WANG Wan-shan. Identification of Cutting Force Coefficients in Different Cutting Edges of Ball-End Milling Cutter [J]. Journal of Northeastern University Natural Science, 2019, 40(9): 1316-1322.
[13]	LI Kui-ning， ZHANG Hong-ji， XIE Yi， FU Chun-yun. Study on Self-Adaptive Multistage Constant Current Charging Based on Electro-Thermal-Aging Coupling Model [J]. Journal of Northeastern University Natural Science, 2019, 40(9): 1323-1329.
[14]	ZHOU Wen-tao， HAN Yue-xin， LI Yan-jun， YANG Jin-lin. Grinding Technical Efficiency Prediction Model Based on Matlab and Particle Swarm Optimization [J]. Journal of Northeastern University Natural Science, 2019, 40(4): 548-552.
[15]	MA Xiao-ying， SUN Zhi-li， ZHANG Yi-bo， ZANG Xu. Optimization of Welding Process Parameters Based on Kriging-PSO Intelligent Algorithm [J]. Journal of Northeastern University Natural Science, 2019, 40(3): 370-375.

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

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments