Skip Nav Destination
Sign In or Register for Account
ASME Press Select Proceedings
Proceedings of the 2010 International Conference on Mechanical, Industrial, and Manufacturing Technologies (MIMT 2010)
By
International Association of Computer Science and Information Technology (IACSIT)
International Association of Computer Science and Information Technology (IACSIT)
Search for other works by this author on:
ISBN:
9780791859544
No. of Pages:
590
Publisher:
ASME Press
Publication date:
2010
eBook Chapter
43 Improved Multiple Minimum Supports Association Rules and Its Applications on Fault Diagnosis
By
Liu Jing
,
Liu Jing
Library YanShan University
QinHangdao, HeBei
, China
Search for other works by this author on:
Ji Hai Peng
,
Ji Hai Peng
College of mechanical engineering YanShan University
QinHangdao, HeBei
, China
Search for other works by this author on:
Zhu Qing Xiang
Zhu Qing Xiang
College of economics and management YanShan University
QinHangdao, HeBei
, China
Search for other works by this author on:
Page Count:
6
-
Published:2010
Citation
Jing, L, Peng, JH, & Xiang, ZQ. "Improved Multiple Minimum Supports Association Rules and Its Applications on Fault Diagnosis." Proceedings of the 2010 International Conference on Mechanical, Industrial, and Manufacturing Technologies (MIMT 2010). Ed. , . ASME Press, 2010.
Download citation file:
The premise of Apriori algorithm is that the frequency and significance of each item in the database are equal or similar, but in the actual practice of fault diagnosis it's not the case. This paper improved it on the basis of Apriori algorithm, utilizing multiple minimum supports to solve the mining of non-frequent item in equipment fault diagnosis; meanwhile a CCWMMS Algorithm based on “credit component values” is proposed, which focus on the item's inconsistent degree of significance in the actual application. This paper also proved this algorithm's exactness and validity in fault diagnosis by actual examples.
Topics:
Fault diagnosis
Abstract
Key Words
1. Introduction
2. Association Rules And Apriori Algorithm
3. Multiple Minimum Supports Association Rules Algorithm
4. Weighted Multiple Minimum Supports Association Rules Algorithm
5. Conclusions
References
This content is only available via PDF.
You do not currently have access to this chapter.
Sign In
Email alerts
Related Chapters
Fault Diagnosis Using an Observers Bank of Dynamic Neural Networks
Intelligent Engineering Systems through Artificial Neural Networks, Volume 16
Fault Diagnosis of Nuclear Components by Kohonen Self-Organizing Maps (PSAM-0068)
Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM)
Application of Improved Wavelet Neural Network to Fault Diagnosis of Pumping Wells
International Conference on Mechanical Engineering and Technology (ICMET-London 2011)
High-Speed Automatic Mechanism Fault Diagnosis Based on Motion Modality and Information Entropy
International Conference on Computer and Computer Intelligence (ICCCI 2011)
Related Articles
A Model-Based Monitoring and Fault Diagnosis Methodology for Free-Form Surface Machining Process
J. Manuf. Sci. Eng (August,2003)
Experimental Study on Fault Caused by Partial Arc Steam Forces and Its Economic Solution
J. Eng. Gas Turbines Power (June,2010)
Growing Structure Multiple Model Systems for Anomaly Detection and Fault Diagnosis
J. Dyn. Sys., Meas., Control (September,2009)