Fault diagnosis studies of face milling cutter using machine learning approach. Issue 2 (June 2016)

• Record Type: Journal Article
• Authors: Madhusudana, CK; Budati, S; Gangadhar, N; Kumar, H; Narendranath, S
• Is Part Of: Journal of low frequency noise, vibration, and active control. Volume 35:Issue 2(2016)
• Page Start: 128
• Page End: 138
• Publication Date: 2016-06
• Subjects: Condition monitoring -- machine learning -- decision tree -- Naïve Bayes; Vibration -- Periodicals; Noise -- Periodicals; Sound -- Periodicals; Damping (Mechanics) -- Periodicals; Damping (Mechanics); Noise; Sound; Vibration; Periodicals; 620.205
• DOI: 10.1177/0263092316644090
• Languages: English
• ISSNs: 1461-3484

Abstract

Successful automation of a machining process system requires an effective and efficient tool condition monitoring system to ensure high productivity, products of desired dimensions, and long machine tool life. As such the component's processing quality and increased system reliability will be guaranteed. This paper presents a classification of healthy and faulty conditions of the face milling tool by using the Naïve Bayes technique. A set of descriptive statistical parameters is extracted from the vibration signals. The decision tree technique is used to select significant features out of all statistical extracted features. The selected features are fed to the Naïve Bayes algorithm. The output of the algorithm is used to study and classify the milling tool condition and it is found that the Naïve Bayes model is able to give 96.9% classification accuracy. Also the performances of the different classifiers are compared. Based on the results obtained, the Naïve Bayes technique can be recommended for online monitoring and fault diagnosis of the face milling tool.