Paper Title :Fault Diagnosis of Drills using Artificial Neural Networks

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

Article Citation :Caleb Vununu ,Seong-Geun Kwon ,Oh-Jun Kwon ,Suk-Hwan Lee ,Ki-Ryongkwon , (2017 ) " Fault Diagnosis of Drills using Artificial Neural Networks " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 16-21, Volume-5,Issue-9

Abstract : 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.

Type : Research paper

Published : Volume-5,Issue-9

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