Fault diagnosis of induction motor using linear discriminant analysis

Authors:
Dae-Jong Lee;Jang-Hwan Park;Dong Hwa Kim;Myung-Geun Chun
Affiliations:
Dept. Of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada;Information & Control Eng, Chungju National University, Chungju, Chungbuk, Korea;Control Engineering, Hanbat National University, Daejeon, Korea;Dept. of Electrical & computer Engineering, Chungbuk National University, Chungju, Chungbuk, Korea
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Year:
2005

Citing 2
Cited 1

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)

Fault diagnosis for induction machines using kernel principal component analysis

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a diagnosis algorithm to detect faults of induction motor using the linear discriminant analysis. First, after reducing the input dimension of the current value vector measured at each period by using the principal component analysis method, we extract the feature vectors for each fault using the linear discriminant analysis. And then, we will diagnosis the condition of an induction motor by using a distance measure between the predefined fault vectors and the input vector. From the various experiments under noisy conditions, we found that the proposed fault detection method could be applied to prevent a fault by diagnosing the conditions of a induction motor in real industrial applications.