Publication Type : Journal Article
Publisher : International Journal of Prognostics and Health Management, Prognostics and Health Management Society
Source : International Journal of Prognostics and Health Management, Prognostics and Health Management Society, Volume 9, Issue 8, Number 1, p.2153-2648 (2018)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045018635&partnerID=40&md5=39530a54870a1d64b38516b4dccffcab
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Mechanical Engineering
Year : 2018
Abstract : To implement the tool condition monitoring system in a metal cutting process, it is necessary to have sensors which will be able to detect the tool conditions to initiate remedial action. There are different signals for monitoring the cutting process which may require different sensors and signal processing techniques. Each of these signals is capable of providing information about the process at different reliability level. To arrive a good, reliable and robust decision, it is necessary to integrate the features of the different signals captured by the sensors. In this paper, an attempt is made to fuse the features of acoustic emission and vibration signals captured in a precision high speed machining center for monitoring the tool conditions. Tool conditions are classified using machine learning classifiers. The classification efficiency of machine learning algorithms are studied in time-domain, frequencydomain and time-frequency domain by feature level fusion of features extracted from vibration and acoustic emission signature. © 2018, Prognostics and Health Management Society. All rights reserved.
Cite this Research Publication : Krishna Kumar P., K. Ramesh Kumar, and Dr. K. I. Ramachandran, “Feature level fusion of vibration and acoustic emission signals in tool condition monitoring using machine learning classifiers”, International Journal of Prognostics and Health Management, vol. 9, no. 8, pp. 2153-2648, 2018.