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http://dx.doi.org/10.9717/kmms.2017.20.2.134

Sound Based Machine Fault Diagnosis System Using Pattern Recognition Techniques  

Vununu, Caleb (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Moon, Kwang-Seok (Dept. of Electronics Engineering, Pukyong National University)
Lee, Suk-Hwan (Dept. of Information Security, Tongmyong University)
Kwon, Ki-Ryong (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Publication Information
Journal of Korea Multimedia Society / v.20, no.2, 2017 , pp. 134-143 More about this Journal
Abstract
Machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines. Generally, it is very difficult to diagnose a machine fault by conventional methods based on mathematical models because of the complexity of the real world systems and the obvious existence of nonlinear factors. This study develops an automatic machine fault diagnosis system that uses pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The sounds emitted by the operating machine, a drill in this case, are obtained and analyzed for the different operating conditions. The specific machine conditions considered in this research are the undamaged drill and the defected drill with wear. Principal component analysis is first used to reduce the dimensionality of the original sound data. The first principal components are then used as the inputs of a neural network based classifier to separate normal and defected drill sound data. The results show that the proposed PCA-ANN method can be used for the sounds based automated diagnosis system.
Keywords
Pattern Recognition; Machine Learning; Machine Fault Diagnosis; Sound Processing; Principal Component Analysis; Artificial Neural Network;
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Times Cited By KSCI : 1  (Citation Analysis)
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