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Fault Diagnosis of Induction Motors using Decision Trees

Tran, Van Tung, Yang, Bo-Suk and Oh, Myung-Suk (2006) Fault Diagnosis of Induction Motors using Decision Trees. In: KSNVE Annual Autumn Conference, April 2006, Tongyoung, Korea.

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Decision tree is one of the most effective and widely used methods for building classification model. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining have considered the decision tree method as an effective solution to their field problems. In this paper, an application of decision tree method to classify the faults of induction motors is proposed. The original data from experiment is dealt with feature calculation to get the useful information as attributes. These data are then assigned the classes which are based on our experience before becoming data inputs for decision tree. The total 9 classes are defined. An implementation of decision tree written in Matlab is used for four
data sets with good performance results

Item Type: Conference or Workshop Item (Paper)
AuthorTran, Van
Subjects: T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering
School of Computing and Engineering > Diagnostic Engineering Research Centre
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Depositing User: Van Tran
Date Deposited: 31 Jan 2013 13:36
Last Modified: 28 Aug 2021 20:13


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