Use of Machine Learning to Improve Condition Monitoring and Vibration Analysis

2020 ◽  
Author(s):  
A. Lauden ◽  
J. Davis ◽  
K. Stohl ◽  
B. Li
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Vibration Analysis ◽  
Improve Condition

scholarly journals Carbide Coated Insert Health Monitoring Using Machine Learning Approach through Vibration Analysis

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Navneet Bohara ◽  
Jegadeeshwaran. R ◽  
Sakthivel G
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Vibration Analysis ◽  
Manufacturing Sector ◽  
Health Condition ◽  
Tool Condition Monitoring ◽  
Learning Approach ◽  
Tool Condition ◽  
Machine Learning Approach ◽  
Carbide Inserts

Growth in the manufacturing sector demands extensive production with precision, accuracy, tolerance, and quality. These essential factors need to be ensured for any kind of job. The listed factors stated above depend upon the condition of the tool used for manufacturing. A lot of methods have been proposed for the tool condition monitoring, based on the data acquired through acquisition techniques. Despite the continuous intensive scientific research for more than a decade, the development of tool condition monitoring is an on-going attempt. The proposed method deals with monitoring the health condition of the carbide inserts using vibration analysis. The statistical information extracted from the vibration signals was analyzed using machine learning approach in order to predict the tool condition.

Sign in / Sign up

Export Citation Format

Share Document