Computer Science – Learning
Scientific paper
2012-01-29
IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 6, No 3, November 2011 ISSN (Online): 1694-0814 www.IJCS
Computer Science
Learning
Scientific paper
In the current competitive world, industrial companies seek to manufacture products of higher quality which can be achieved by increasing reliability, maintainability and thus the availability of products. On the other hand, improvement in products lifecycle is necessary for achieving high reliability. Typically, maintenance activities are aimed to reduce failures of industrial machinery and minimize the consequences of such failures. So the industrial companies try to improve their efficiency by using different fault detection techniques. One strategy is to process and analyze previous generated data to predict future failures. The purpose of this paper is to detect wasted parts using different data mining algorithms and compare the accuracy of these algorithms. A combination of thermal and physical characteristics has been used and the algorithms were implemented on Ahanpishegan's current data to estimate the availability of its produced parts. Keywords: Data Mining, Fault Detection, Availability, Prediction Algorithms.
Amooee Golriz
Bagheri-Dehnavi Malihe
Minaei-Bidgoli Behrouz
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A Comparison Between Data Mining Prediction Algorithms for Fault Detection(Case study: Ahanpishegan co.) has received 3 rating(s) and 2 review(s), resulting in an average rating of 3.98 on a scale from 1 to 5. The overall rating for this scientific paper is very good.
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Anonymous visitor
having a case study makes it a real use
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Anonymous visitor
Fugures in paper have some issues !
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