C. K. Madhusudana1, N. Gangadhar1, Hemantha Kumar, Kumar,*,1, S. Narendranath1
Structural Durability & Health Monitoring, Vol.12, No.2, pp. 111-127, 2018, DOI: 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.… More >
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