Feature level fusion of vibration and acoustic emission signals in tool condition monitoring using machine learning classifiers

P. Krishnakumar, K. Rameshkumar, and K. I. Ramachandran
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Full Paper
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This research is supported by Directorate of Extramural Research & Intellectual Property Rights (ER & IPR), Defense Research and Development Organisation (DRDO), ERIP/ER/0803740/M/01/1194
ijphm_18_008.pdf442.49 KBMarch 27, 2018 - 11:09pm

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, frequency-domain and time-frequency domain by feature level fusion of features extracted from vibration and acoustic emission signature.

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Submission Keywords: 
Tool condition monitoring; vibration; acoustic emission; feature level fusion; machine learning algorithms
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
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