NDLI: Bayes error based feature selection: An electric motors fault detection case study

Content Provider	IEEE Xplore Digital Library
Author	Ciabattoni, L. Cimini, G. Ferracuti, F. Grisostomi, M. Ippoliti, G. Pirro, M.
Copyright Year	2015
Description	Author affiliation: Dept. of Inf. Eng., Univ. Politec. delle Marche, Ancona, Italy (Ciabattoni, L.; Cimini, G.; Ferracuti, F.; Grisostomi, M.; Ippoliti, G.; Pirro, M.)
Abstract	In the modern industrial sector there is a growing interest on electric motors safety, reliability and maintainability. In this context health monitoring and fault detection are crucial tasks to be performed. In this paper we introduce a univariate filter method based on Bayes error for feature selection in a fault detection scenario. The feature selection algorithm firstly estimates the probability density function of the data. At a second stage we compute the PDFs intersection area which is related to the Bayes error. Finally we choose the features with the minimum Bayes error. In order to properly test the proposed algorithm, a starter motor assembly line has been considered as a case study. Features extraction is performed on statistical time and frequency domain analysis while the quadratic classifier is used for the final fault detection. Performances of the proposed approach have been compared with those of Relieff and SFS algorithms on a 649 motors data set. Results show that our method outperforms the other two in terms of Area Under Curve - Receiver Operating Characteristic (AUC-ROC).
Sponsorship	IEEE
Starting Page	003893
Ending Page	003898
File Size	316301
Page Count	6
File Format	PDF
e-ISBN	9781479917624
DOI	10.1109/IECON.2015.7392707
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-09
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Fault detection DC motors Vibrations Time-frequency analysis Estimation Electric motors Feature selection Bayes methods Probability density function Vibration measurement
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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