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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks Volume 18
Editor
ISBN-10:
0791802823
ISBN:
9780791802823
No. of Pages:
700
Publisher:
ASME Press
Publication date:
2008
eBook Chapter
88 Classification Model to Detect Failed HDD Components
By
Danaipong Chetchotsak
,
Danaipong Chetchotsak
Dept. of Industrial Engineering, Faculty of Engineering,
Khon Kaen University
, Khon Kaen, 40002
, Thailand
; canai@kku.ac.th
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Wanida Kanarkard
Wanida Kanarkard
Dept. of Computer Engineering, Faculty of Engineering,
Khon Kaen University
, Khon Kaen, 40002
, Thailand
; wanida@kku.ac.th
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Page Count:
7
-
Published:2008
Citation
Chetchotsak, D, & Kanarkard, W. "Classification Model to Detect Failed HDD Components." Intelligent Engineering Systems through Artificial Neural Networks Volume 18. Ed. Dagli, CH. ASME Press, 2008.
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In HDD manufacturing processes, a real time monitoring tool used to detect failed HDD components has become an important issue. Once we can monitor the failed components, we can remove them from the production process. This could help the HDD manufacturers to reduce the production time and improve productivity. The focus of this paper is to construct a classification model to detect failed HDD components and also to study and choose potential learned models. The learned models in this paper include the C5.0 decision tree algorithm, CHAID, QUEST, neural network, and the committee machine. Experimental results reveal that the committee machine of C5.0 has the most robust performance and very practical computation time.
Topics:
Horizontal directional drilling
Abstract
Introduction
The Classification Models
Experiment
Results
Conclusion
Acknowledgements
References
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