Fault diagnosis of helical gear box using decision tree through vibration signals
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Date
2013
Authors
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Journal ISSN
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Publisher
RAMS Consultants
Abstract
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. © RAMS Consultants.
Description
Keywords
Classification (of information), Computer aided diagnosis, Data mining, Failure analysis, Fault detection, Feature Selection, Learning systems, Vibrations (mechanical), Decision-tree algorithm, Effect of feature, Faults diagnosis, Faulty condition, Features selection, Gear fault diagnosis, Helical gear box, Machine learning approaches, Statistical features, Vibration signal, Decision trees
Citation
International Journal of Performability Engineering, 2013, 9, 2, pp. 221-233