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Vaithiyanathan, Muralidharan
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Tool Condition Monitoring in Face Milling Process Using Decision Tree and Statistical Features of Vibration Signal

B S Abdur Rahman Crescent Institute of Science & Technology-Pradeep Kumar Durairaj, Muralidharan Vaithiyanathan
  • Technical Paper
  • 2019-28-0142
To be published on 2019-10-11 by SAE International in United States
In milling process, the quality of the machined component is highly influenced by the condition of the tool. Hence, monitoring the condition of the tool becomes essential. A suitable mechanism needs to be devised in order to monitor the condition of the tool. To achieve this, condition monitoring of milling tool is taken up for the study. In this work, the condition of the tool is classified as good tool and tool with common faults in face milling process such as flank wear, worn out and breakage of the tool based on machine learning approach using statistical feature and decision tree technique. Vibration signals of the milling tool are obtained during machining of mild steel. Statistical features are extracted from the obtained signal, in which the important features are selected using decision tree. The selected features are given as the input to the same algorithm. The output of the algorithm is utilized for classifying the different conditions of the tool. The experimental results show that the accuracy of decision tree technique is at the acceptable…
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Fault Detection in Single Stage Helical Planetary Gearbox Using Artificial Neural Networks (ANN) and Decision Tree with Histogram Features

BSACIST-Syed Shaul Hameed, Muralidharan Vaithiyanathan, Mahendran Kesavan
  • Technical Paper
  • 2019-28-0151
To be published on 2019-10-11 by SAE International in United States
Drive train failures are most common in wind turbines. Lots of effort has been made to improve the reliability of the gearbox but the truth is that these efforts do not provide a lifetime solution. Majority of failures are caused by bearing and gearbox. It also states that wind turbine gearbox failure causes the highest downtime as the repair has to be done at Original Equipment Manufacturer [OEM]. This work aims to predict the failures in planetary gearbox using fault diagnosis technique and machine learning algorithms. In the proposed method the failing parts of the planetary gearbox are monitored with the help of accelerometer sensor mounted on the planetary gearbox casing which will record the vibrations. A prototype has been fabricated as a miniature of single stage planetary gearbox. The vibrations of the healthy gearbox, sun defect, planet defect and ring defect under loaded conditions are obtained. The signals show the performance characteristics of the gearbox condition. These characteristics and their number of occurrences were plotted in a histogram graph. Predominant statistical features which represent…