condition monitoring of face milling tool using k-star algorithm and histogram features of vibration signal

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ID: 172845
2016
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Abstract
This 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.
Reference Key
madhusudana2016engineeringcondition Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;C.K. Madhusudana;Hemantha Kumar;S. Narendranath
Journal International journal of molecular sciences
Year 2016
DOI
10.1016/j.jestch.2016.05.009
URL
http://www.sciencedirect.com/science/article/pii/S2215098616302142
Keywords
Machine learning fault diagnosis decision tree civil engineering (general)

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