<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Journal-bearings play a significant role in industrial applications and the necessity of condition monitoring with nondestructive tests is increasing. This paper deals a proper fault detection technique based on power spectral density (PSD) of vibration signals in combination with K-Nearest Neighbor and Support Vector Machine (SVM). The frequency domain vibration signals of an internal combustion engine with three journal-bearing conditions were gained, corresponding to, (i) normal, (ii) corrosion and (iii) excessive wear. The features of the PSD values of vibration signals were extracted using statistical and vibration parameters. The extracted features were used as inputs to the KNN and SVM for three-class identification. The roles of PSD technique and the KNN and SVM classifiers were investigated. Results showed that the accuracy rate of fault diagnosis was 100%. Also, the results demonstrated that the combined PSD-SVM model had the potential for fault diagnosis of engine journal-bearing.</p>
</abstract>

Journal-bearings play a significant role in industrial applications and the necessity of condition monitoring with nondestructive tests is increasing. This paper deals a proper fault detection technique based on power spectral density (PSD) of vibration signals in combination with K-Nearest Neighbor and Support Vector Machine (SVM). The frequency domain vibration signals of an internal combustion engine with three journal-bearing conditions were gained, corresponding to, (i) normal, (ii) corrosion and (iii) excessive wear. The features of the PSD values of vibration signals were extracted using statistical and vibration parameters. The extracted features were used as inputs to the KNN and SVM for three-class identification. The roles of PSD technique and the KNN and SVM classifiers were investigated. Results showed that the accuracy rate of fault diagnosis was 100%. Also, the results demonstrated that the combined PSD-SVM model had the potential for fault diagnosis of engine journal-bearing.

An Appropriate Procedure for Detection of Journal-Bearing Fault using Power Spectral Density, K-Nearest Neighbor and Support Vector Machine

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Journal-bearings play a significant role in industrial applications and the necessity of condition monitoring with nondestructive tests is increasing. This...

An Appropriate Procedure for Detection of Journal-Bearing Fault using Power Spectral Density, K-Nearest Neighbor and Support Vector Machine

Pubblicato online: 01 set 2012

Pagine: 685 - 700

Accettato: 05 ago 2012

DOI: https://doi.org/10.21307/ijssis-2017-502

© 2012 A. Moosavian et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.