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Use of Discrete Wavelet Features and Support Vector Machine for Fault Diagnosis of Face Milling Tool

C. K. Madhusudana1, N. Gangadhar1, Hemantha Kumar, Kumar,*,1, S. Narendranath1
National Institute of Technology Karnataka, Surathkal, Mangalore , Pin-575025, India.
*Corresponding Author: Hemantha Kumar. Email: .

Structural Durability & Health Monitoring 2018, 12(2), 111-127. https://doi.org/ 10.3970/sdhm.2018.01262

Abstract

This paper presents the fault diagnosis of face milling tool based on machine learning approach. While machining, spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are acquired. A set of discrete wavelet features is extracted from the vibration signals using discrete wavelet transform (DWT) technique. The decision tree technique is used to select significant features out of all extracted wavelet features. C-support vector classification (C-SVC) and ν-support vector classification (ν-SVC) models with different kernel functions of support vector machine (SVM) are used to study and classify the tool condition based on selected features. From the results obtained, C-SVC is the best model than ν-SVC and it can be able to give 94.5% classification accuracy for face milling of special steel alloy 42CrMo4.

Keywords

Fault diagnosis, face milling, decision tree, discrete wavelet transform, support vector machine.

Cite This Article

Madhusudana, C. K., Gangadhar, N., Kumar,, H. K., Narendranath, S. (2018). Use of Discrete Wavelet Features and Support Vector Machine for Fault Diagnosis of Face Milling Tool. Structural Durability & Health Monitoring, 12(2), 111–127.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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