caa a22 4500 463207157 CHVBK 20180405153129.0 cr unu---uuuuu 170326e20070601xx s 000 0 eng 10.1007/s10462-008-9081-6 doi (NATIONALLICENCE)springer-10.1007/s10462-008-9081-6 Using support vector machines and acoustic noise signal for degradation analysis of rotating machinery [Elektronische Daten] [Patricia Scanlon, Susan Bergin] 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. Springer Science+Business Media B.V., 2008 Support vector machines nationallicence Degradation analysis nationallicence Acoustic signal processing nationallicence Feature selection nationallicence Scanlon Patricia Bell Labs Ireland - Alcatel Lucent, Dublin 15, Ireland aut Bergin Susan Department of Computer Science, NUI Maynooth, Maynooth, Ireland aut Artificial Intelligence Review Springer Netherlands 28/1(2007-06-01), 1-15 0269-2821 28:1<1 2007 28 10462 https://doi.org/10.1007/s10462-008-9081-6 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10462-008-9081-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Scanlon Patricia Bell Labs Ireland - Alcatel Lucent, Dublin 15, Ireland aut NATIONALLICENCE 700 1- Bergin Susan Department of Computer Science, NUI Maynooth, Maynooth, Ireland aut NATIONALLICENCE 773 0- Artificial Intelligence Review Springer Netherlands 28/1(2007-06-01), 1-15 0269-2821 28:1<1 2007 28 10462 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer