Fault diagnosis system using LPC coefficients and neural network

As rotating machines perform an important role in industrial applications, many researchers have developed various condition monitoring system and fault diagnosis system by applying various techniques such as signal processing and pattern recognition. Recently, fault diagnosis systems using artifici...

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Hauptverfasser:	Hyungseob Han, Sangjin Cho, Uipil Chong
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Biological system modeling component Educational institutions Equations fault diagnosis Feature extraction LPC coefficients Mathematical model Monitoring neural network
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creator	Hyungseob Han Sangjin Cho Uipil Chong
description	As rotating machines perform an important role in industrial applications, many researchers have developed various condition monitoring system and fault diagnosis system by applying various techniques such as signal processing and pattern recognition. Recently, fault diagnosis systems using artificial neural network have been proposed. This paper proposes the neural-network-based fault diagnosis system using the proper feature vectors by LPC (linear predictive coding) coefficients. This method has not been reported yet. For the effective fault diagnosis, a MLP (multi-layer perceptron) network is used. From the experiment results, the proposed system shows a perfect fault diagnosis for each faulty case.
doi_str_mv	10.1109/IFOST.2010.5667999
format	Conference Proceeding
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source	IEEE Electronic Library (IEL) Conference Proceedings
subjects	Biological system modeling component Educational institutions Equations fault diagnosis Feature extraction LPC coefficients Mathematical model Monitoring neural network
title	Fault diagnosis system using LPC coefficients and neural network
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