A Comparison Between Data Mining Prediction Algorithms for Fault Detection(Case study: Ahanpishegan co.)
This is an incremental study applying existing data mining methods to a specific industrial case for fault detection.
The paper compared data mining prediction algorithms for fault detection in industrial machinery at Ahanpishegan company, using thermal and physical characteristics to predict wasted parts and estimate availability, but did not report specific accuracy numbers or results.
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.