Improving the Performance of Machine Learning Classifiers for Image Category Identification Using Feature Level Fusion of Otsu Segmentation Augmented with Thepade's N-Ary Sorted Block Truncation Coding

2018 ◽  
Author(s):  
Sudeep D. Thepade ◽  
Yugali Bafna
Keyword(s):  
Machine Learning ◽  
Block Truncation Coding ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Feature Level Fusion ◽  
Level Fusion

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

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
P. Krishnakumar ◽  
K. Rameshkumar ◽  
K. I. Ramachandran
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Cutting Process ◽  
Machine Learning Classifiers ◽  
Tool Condition ◽  
Learning Classifiers ◽  
Feature Level Fusion ◽  
Level Fusion

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.

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