Condition monitoring of FSW tool using vibration analysis – A machine learning approach

2020 ◽  
Vol 27 ◽  
pp. 2970-2974
Author(s):  
K. Balachandar ◽  
R. Jegadeeshwaran ◽  
D. Gandhikumar
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Vibration Analysis ◽  
Learning Approach ◽  
Machine Learning Approach

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.

A machine learning approach for the condition monitoring of rotating machinery

2014 ◽  
Vol 28 (1) ◽  
pp. 61-71 ◽  
Author(s):  
Dimitrios Kateris ◽  
Dimitrios Moshou ◽  
Xanthoula-Eirini Pantazi ◽  
Ioannis Gravalos ◽  
Nader Sawalhi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Rotating Machinery ◽  
Learning Approach ◽  
Machine Learning Approach

Engine gearbox fault diagnosis using machine learning approach

2018 ◽  
Vol 24 (3) ◽  
pp. 345-357 ◽  
Author(s):  
Kiran Vernekar ◽  
Hemantha Kumar ◽  
Gangadharan K.V.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Condition Monitoring ◽  
Experimental Result ◽  
Maintenance Cost ◽  
Learning Approach ◽  
Content Type ◽  
Machine Learning Approach

Purpose Bearings and gears are major components in any rotatory machines and, thus, gained interest for condition monitoring. The failure of such critical components may cause an increase in down time and maintenance cost. Condition monitoring using the machine learning approach is a conceivable solution for the problem raised during the operation of the machinery system. The paper aims to discuss these issues. Design/methodology/approach This paper aims engine gearbox fault diagnosis based on a decision tree and artificial neural network algorithm. Findings The experimental result (classification accuracy 85.55 percent) validates that the proposed approach is an effective method for engine gearbox fault diagnosis. Originality/value This paper attempts to diagnose the faults in engine gearbox based on the machine learning approach with the combination of statistical features of vibration signals, decision tree and multi-layer perceptron neural network techniques.

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