Support vector machine and K-nearest neighbour for unbalanced fault detection
Journal of Quality in Maintenance Engineering
ISSN: 1355-2511
Article publication date: 4 March 2014
Abstract
Purpose
The purpose of this paper is to develop an appropriate approach for detecting unbalanced fault in rotating machines using KNN and SVM classifiers.
Design/methodology/approach
To fulfil this goal, a fault diagnosis approach based on signal processing, feature extraction and fault classification, was used. Vibration signals were acquired from a designed experimental system with three conditions, namely, no load, balanced load and unbalanced load. FFT technique was applied to transform the vibration signals from time-domain into frequency-domain. In total, 29 feature parameters were extracted from FFT amplitude of the signals. SVM and KNN were employed to classify the three different conditions. The performances of the two classifiers were obtained under different values of their parameter.
Findings
The experimental results show the potential application of SVM for machine fault diagnosis.
Practical implications
The results demonstrate that the proposed approach can be used effectively for detecting unbalanced condition in rotating machines.
Originality/value
In this paper, an intelligent approach for unbalanced fault detection was proposed based on supervised learning method. Also, a performance comparison was made between KNN and SVM in fault classification. In addition, this approach gave a high level of classification accuracy. The proposed intelligent approach can be used for other mechanical faults.
Keywords
Acknowledgements
The authors would like to acknowledge Irankhodro Powertrain Company (IPCo) for its support and contribution to this study.
Citation
Moosavian, A., Ahmadi, H., Sakhaei, B. and Labbafi, R. (2014), "Support vector machine and K-nearest neighbour for unbalanced fault detection", Journal of Quality in Maintenance Engineering, Vol. 20 No. 1, pp. 65-75. https://doi.org/10.1108/JQME-04-2012-0016
Publisher
:Emerald Group Publishing Limited
Copyright © 2014, Emerald Group Publishing Limited