NDLI: The role of subclasses in machine diagnostics

Content Provider	IEEE Xplore Digital Library
Author	Skurichina, M. Ypma, A. Duin, R.P.W.
Copyright Year	2000
Description	Author affiliation: Dept. of Appl. Phys., Delft Univ. of Technol., Netherlands (Skurichina, M.)
Abstract	In machine diagnostics it is difficult to collect for learning all possible operating modes of machine functioning. Some of the operating modes are often missing. In these circumstances, it is important to know which modes (subclasses) are the most valuable for successful machine diagnosis. It is also of interest to investigate the usefulness of noise injection to cover the missing operating modes in the data. In this paper, we study the importance of selecting different operating modes of a water-pump and using them for learning in both 2-class and 4-class problems. We show that the operating modes representing different running speeds are more valuable than those representing machine loads. We also demonstrate that the 2-nearest neighbours directed noise injection is useful when filing in missing operating modes in the data.
Starting Page	668
Ending Page	671
File Size	413614
Page Count	4
File Format	PDF
ISBN	0769507506
ISSN	10514651
DOI	10.1109/ICPR.2000.906163
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-09-03
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gaussian noise Filling Costs Pattern recognition Physics Marine technology Working environment noise Machine learning Niobium Scattering
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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