Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/11292
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dc.contributor.authorSugumaran, V.
dc.contributor.authorJain, D.
dc.contributor.authorAmarnath, M.
dc.contributor.authorKumar, H.
dc.date.accessioned2020-03-31T08:31:04Z-
dc.date.available2020-03-31T08:31:04Z-
dc.date.issued2013
dc.identifier.citationInternational Journal of Performability Engineering, 2013, Vol.9, 2, pp.221-233en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/11292-
dc.description.abstractThis 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.en_US
dc.titleFault diagnosis of helical gear box using decision tree through vibration signalsen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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