Bitmap-Base Association Rule Optimization Algorithm and Application in Equipment Fault Diagnosis

Qing Xiang Zhu; Lei Zhang; Jing Liu; Peng Sha Jiao; Hong Hong Hao

doi:10.4028/www.scientific.net/AMR.308-310.1669

Paper Titles

Geotechnical Parameters of Composite Soil
p.1651

Defect Contour Matching Based on Similarity Measure for 3D Reconstruction
p.1656

Chemical Constituents of Plumbago Zeylanica L.
p.1662

The Optimization of Edge-Shape Parameter for Curve-Edge Milling Insert Based on Thermal-Mechanic Coupling Physical Field
p.1665

Bitmap-Base Association Rule Optimization Algorithm and Application in Equipment Fault Diagnosis
p.1669

Vehicle Active Suspension with Four-Degrees of Freedom of Optimizing the Value of K Based on Genetic Algorithm
p.1673

Design of Temperature Monitoring Network Based on CC1110 Wireless Microcontroller
p.1679

Prediction and Discuss of Strap Mining Subsidence by Numerical Simulation Analysis and its Engineering Apply
p.1683

Projective Synchronzing a Novel Fractional Hyperchaotic System Based on a Approach Configuring a Special Matrix
p.1688

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 308-310Bitmap-Base Association Rule Optimization...

Bitmap-Base Association Rule Optimization Algorithm and Application in Equipment Fault Diagnosis

Abstract:

The Apriori algorithm needs to continue scanning the database and may provide mass candidate itemsets in fault diagnosis process which causes the mining speed too slow and the computer memory too large. According to these weaknesses this paper presents the bitmap-base association rule optimization algorithm (BARO). The BARO improves the data structure to reduce the scanning frequency of database and compresses the matrix to reduce the quantity of candidate itemsets in order to improve the speed of equipment fault diagnosis. It proves that BARO is superior to Apriori algorithm in equipment fault diagnosis of efficiency and mining speed by a concrete example.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 308-310)

Pages:

1669-1672

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.308-310.1669

Citation:

Cite this paper

Online since:

August 2011

Authors:

Qing Xiang Zhu, Lei Zhang, Jing Liu, Peng Sha Jiao, Hong Hong Hao

Keywords:

Apriori Algorithm, Association Rule, Equipment Fault Diagnosis, Matrix Algorithms

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Yanping Zhao, Guanxin Yao and Jun Chen: Equipment Management And Maintenance (Chemical Industry Publishing House Publications, Beijing 2004). in Chinese.

Google Scholar

[2] Richard J.Roiger and Michael W.Geatz: The Data Mining Course (Tsinghua University Publications, Beijing 2003).

Google Scholar

[3] Zhirui Liang, Peng Chen, Haifeng Su: Electric Power Automation Equipment. Vol.06 (2006), pp.123-125. in Chinese.

Google Scholar

[4] Jing Liu, Haipeng Ji, Qingxiang Zhu: Industrial Engineering Journal. Vol.13 (2010), pp.108-111. in Chinese.

Google Scholar

[5] Jun Liu, Yu Wu: Sciencepaper Online. http://www/paper.edu.cn/index.php/default/releasepaper/content/201007-523. in Chinese.

Google Scholar