Title
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
Recommended Citation
Razavi-Far, Roozbeh; Hallaji, Ehsan; Saif, Mehrdad; and Rueda, Luis. (2017). A hybrid scheme for fault diagnosis with partially labeled sets of observations. Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, 2017-December, 61-67.
https://scholar.uwindsor.ca/electricalengpub/155