Scanlon, Patricia and Bergin, Susan (2007) Using Support Vector Machines and Acoustic Noise Signal for Degradation Analysis of Rotating Machinery. Artificial Intelligence Review, 28 (1). pp. 1-15. ISSN 0269-2821

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Abstract

An automated approach to degradation analysis is proposed that uses a rotating machine’s acoustic signal to determine Remaining Useful Life (RUL). High resolution spectral features are extracted from the acoustic data collected over the entire lifetime of the machine. A novel approach to the computation of Mutual Information based Feature Subset Selection is applied, to remove redundant and irrelevant features, that does not require class label boundaries of the dataset or spectral locations of developing defect to be known or pre-estimated. Using subsets of the feature space, multi-class linear and Radial Basis Function (RBF) Support Vector Machine (SVM) classifiers are developed and a comparison of their performance is provided. Performance of all classifiers is found to be very high, 85 to 98%, with RBF SVMs outperforming linear SVMs when a smaller number of features are used. As larger numbers of features are used for classification, the problem space becomes more linearly separable and the linear SVMs are shown to have comparable performance. A detailed analysis of the misclassifications is provided and an approach to better understand and interpret costly misclassifications is discussed. While defining class label boundaries using an automated k-means clustering algorithm improves performance with an accuracy of approximately 99%, further analysis shows that in 88% of all misclassifications the actual class of failure had the next highest probability of occurring. Thus, a system that incorporates probability distributions as a measure of confidence for the predicted RUL would provide additional valuable information for scheduling preventative maintenance.

Item Type:	Article
Keywords:	Support vector machines; Degradation analysis; Acoustic signal processing; Feature selection;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	8683
Depositing User:	Dr. Susan Bergin
Date Deposited:	25 Aug 2017 11:09
Journal or Publication Title:	Artificial Intelligence Review
Publisher:	Springer Verlag
Refereed:	Yes
Funders:	IDA Ireland
URI:	https://link.springer.com
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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