International Journal of Advance Computational Engineering and Networking (IJACEN)
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Statistics report
Feb. 2020
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 83
Paper Published : 1216
No. of Authors : 3056
  Journal Paper

Paper Title
Fault Diagnosis of Drills using Artificial Neural Networks

Machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines using the signals generated by these machines. This study aims to develop an automatic machine fault diagnosis system that uses pattern recognition techniques such as artificial neural networks. The sounds emitted by the healthy and faulty drills are obtained and analyzed. Unlike the conventional methods that focus on the time domain, we explore here the effectiveness of the frequency domain components and demonstrate in the same time the ineffectiveness of the time domain analysis of the sounds produced by drills. First, the power spectrum of the sounds are extracted as the features of the signals. Secondly, the extracted power spectrum components are given to a neural network based classifier to perform the diagnosis. The results show that the proposed method can be used for the sounds based automatic diagnosis system. Index terms- Fault diagnosis, Machine learning, Artificial neural network, Sound processing.

Author - Caleb Vununu, Seong-Geun Kwon, Oh-Jun Kwon, Suk-Hwan Lee, Ki-Ryongkwon

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| Published on 2017-11-23
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