Please use this identifier to cite or link to this item: https://idr.l4.nitk.ac.in/jspui/handle/123456789/10360
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMadhusudana, C.K.
dc.contributor.authorKumar, H.
dc.contributor.authorNarendranath, S.
dc.date.accessioned2020-03-31T08:19:00Z-
dc.date.available2020-03-31T08:19:00Z-
dc.date.issued2016
dc.identifier.citationEngineering Science and Technology, an International Journal, 2016, Vol.19, 3, pp.1543-1551en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/10360-
dc.description.abstractThis paper deals with the fault diagnosis of the face milling tool based on machine learning approach using histogram features and K-star algorithm technique. Vibration signals of the milling tool under healthy and different fault conditions are acquired during machining of steel alloy 42CrMo4. Histogram features are extracted from the acquired signals. The decision tree is used to select the salient features out of all the extracted features and these selected features are used as an input to the classifier. K-star algorithm is used as a classifier and the output of the model is utilised to study and classify the different conditions of the face milling tool. Based on the experimental results, K-star algorithm is provided a better classification accuracy in the range from 94% to 96% with histogram features and is acceptable for fault diagnosis. 2016 Karabuk Universityen_US
dc.titleCondition monitoring of face milling tool using K-star algorithm and histogram features of vibration signalen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.