A Comparison Between Data Mining Prediction Algorithms for Fault Detection-Case study Ahanpishegan Co.
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 Ahanpishegans current data to estimate the availability of its produced parts.
Keywords: Data Mining, Fault Detection, Availability, Prediction Algorithms.
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ABOUT THE AUTHORS
Golriz Amooee
Golriz Amooee was born in Tehran, Iran in 1987. She received her B.S. degree with a first class Honors in Information Technology from Islamic Azad University, Parand Branch, Iran, in 2009 and currently she is a M.S. student in the Department of Information Technology at University of Qom, Iran. She specializes in the field of Customer Relationship Management (CRM), Information Security Management and ISO 27001.
Behrouz Minaei-Bidgoli
Dr Behrouz Minaei-Bidgoli obtained his Ph.D. degree from Michigan State University, East Lansing, Michigan, USA, in the field of Data Mining and Web-Based Educational Systems in Computer Science and Engineering Department. He is working as an assistant professor in Computer Engineering Department of Iran University of Science & Technology, Tehran, Iran. He is also leading at a Data and Text Mining research group in Computer Research Center of Islamic Sciences, NOOR co. Qom, Iran, developing large scale NLP and Text Mining projects for Farsi and Arabic languages.
Malihe Bagheri-Dehnavi
Malihe Bagheri-Dehnavi was born in Qom, Iran in 1988. She received her B.S. degree in and currently she is a M.S. student in the Department of Information Technology at University of Qom, Iran.
Golriz Amooee
Golriz Amooee was born in Tehran, Iran in 1987. She received her B.S. degree with a first class Honors in Information Technology from Islamic Azad University, Parand Branch, Iran, in 2009 and currently she is a M.S. student in the Department of Information Technology at University of Qom, Iran. She specializes in the field of Customer Relationship Management (CRM), Information Security Management and ISO 27001.
Behrouz Minaei-Bidgoli
Dr Behrouz Minaei-Bidgoli obtained his Ph.D. degree from Michigan State University, East Lansing, Michigan, USA, in the field of Data Mining and Web-Based Educational Systems in Computer Science and Engineering Department. He is working as an assistant professor in Computer Engineering Department of Iran University of Science & Technology, Tehran, Iran. He is also leading at a Data and Text Mining research group in Computer Research Center of Islamic Sciences, NOOR co. Qom, Iran, developing large scale NLP and Text Mining projects for Farsi and Arabic languages.
Malihe Bagheri-Dehnavi
Malihe Bagheri-Dehnavi was born in Qom, Iran in 1988. She received her B.S. degree in and currently she is a M.S. student in the Department of Information Technology at University of Qom, Iran.
