Fault Diagnosis of Helical Gear Box using Decision Tree through Vibration Signals
Volume 9, Number 2, March 2013 - Paper 9 - pp. 221-234
V. SUGUMARAN1, DEEPAK JAIN2, M. AMARNATH3 and HEMANTHA KUMAR41,2 SMBS, VIT University, Chennai Campus, Vandalur-kelambakam road, Chennai-600048,
3 Indian Institute of Information Technology Design and Manufacturing Jabalpur, Jabalpur
4 National Institute of Technology Karnataka, Surathkal, Mangalore, Karnataka, INDIA.
(Received on June 05, 2012 and revised on January 14, 2013)
This paper uses vibration signals acquired from gears in good and simulated faulty conditions for the purpose of fault diagnosis through machine learning approach. The descriptive statistical features were extracted from vibration signals and the important ones were selected using decision tree (dimensionality reduction). The selected features were then used for classification using J48 decision tree algorithm. The paper also discusses the effect of various parameters on classification accuracy.
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