A hybrid scheme for fault diagnosis with partially labeled sets of observations

Document Type

Conference Proceeding

Publication Date

1-1-2017

Publication Title

Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017

Volume

2017-December

First Page

61

Keywords

bearing defects, dimensionality reduction, Fault diagnosis, induction motors, semi-supervised learning

Last Page

67

Abstract

Machine learning techniques are widely used for diagnosing faults to guarantee the safe and reliable operation of the systems. Among various techniques, semi-supervised learning can help in diagnosing faulty states and decision making in partially labeled data, where only a few number of labeled observations along with a large number of unlabeled observations are collected from the process. Thus, it is crucial to conduct a critical study on the use of semi-supervised techniques for both dimensionality reduction and fault classification. In this work, three state-of-the- A rt semi-supervised dimensionality reduction techniques are used to produce informative features for semi-supervised fault classifiers. This study aims to achieve the best pair of the semisupervised dimensionality reduction and classification techniques that can be integrated into the diagnostic scheme for decision making under partially labeled sets of observations.

DOI

10.1109/ICMLA.2017.0-177

ISBN

9781538614174

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