Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-BearingReportar como inadecuado




Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

Shock and Vibration - Volume 20 2013, Issue 2, Pages 263-272

Department of Mechanical Engineering of Agricultural Machinery, University of Tehran, Karaj, Iran

Received 18 March 2012; Revised 1 August 2012

Copyright © 2013 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion IC engine based on power spectral density PSD technique and two classifiers, namely, K-nearest neighbor KNN and artificial neural network ANN. Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count N were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine.





Autor: A. Moosavian, H. Ahmadi, A. Tabatabaeefar, and M. Khazaee

Fuente: https://www.hindawi.com/



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