Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/11461
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dc.contributor.authorVernekar, K.
dc.contributor.authorKumar, H.
dc.contributor.authorGangadharan, K.V.
dc.date.accessioned2020-03-31T08:31:27Z-
dc.date.available2020-03-31T08:31:27Z-
dc.date.issued2018
dc.identifier.citationJournal of Quality in Maintenance Engineering, 2018, Vol.24, 3, pp.345-357en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/11461-
dc.description.abstractPurpose: Bearings and gears are major components in any rotatory machines and, thus, gained interest for condition monitoring. The failure of such critical components may cause an increase in down time and maintenance cost. Condition monitoring using the machine learning approach is a conceivable solution for the problem raised during the operation of the machinery system. The paper aims to discuss these issues. Design/methodology/approach: This paper aims engine gearbox fault diagnosis based on a decision tree and artificial neural network algorithm. Findings: The experimental result (classification accuracy 85.55 percent) validates that the proposed approach is an effective method for engine gearbox fault diagnosis. Originality/value: This paper attempts to diagnose the faults in engine gearbox based on the machine learning approach with the combination of statistical features of vibration signals, decision tree and multi-layer perceptron neural network techniques. 2018, Emerald Publishing Limited.en_US
dc.titleEngine gearbox fault diagnosis using machine learning approachen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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