NDLI: Fault detection with an adaptive distance for the k-Nearest neighbors rule

Content Provider	IEEE Xplore Digital Library
Author	Verdier, G. Ferreira, A.
Copyright Year	2009
Description	Author affiliation: Ecole Nationale Supérieure des Mines de Saint-Etienne, Centre de Microélectronique de Provence, Gardanne, France (Verdier, G.; Ferreira, A.)
Abstract	In recent years, fault detection has become a crucial issue for many industrial fields, notably the semiconductormanufacturing where process control engineers constantly try to improve the equipment productivity by detecting as quickly as possible an abnormal behavior. Due to the number of variables and the correlations between them in this type of applications, statistical methods dealing with fault detection need to be multivariate. Usually, the multivariate control chart procedures used in the industry derived from the Hotelling $T^{2}.$ However, this rule can only be used when the observations are generated by a Gaussian distribution, an assumption rarely satisfied in practice. An alternative consists to apply nonparametric control charts for which there is no assumption needed on the distribution. A nonparametric rule, the k-Nearest Neighbors Detection rule is studied in this paper. The approach consists in evaluating the distance of an observation to its nearest neighbors and declaring a fault if this distance is too large. In this paper, a new adaptive Mahalanobis distance is proposed. It takes into account the local correlation structure of the data and then improves the number of faults detected for a fixed false alarm rate, compared to a classic distance such as the Euclidean distance.
Starting Page	1273
Ending Page	1278
File Size	1333476
Page Count	6
File Format	PDF
ISBN	9781424441358
DOI	10.1109/ICCIE.2009.5223844
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-06
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Productivity Statistical analysis Industrial control Adaptive Mahalanobis distance Process control Hotelling $T^{2}$ Multivariate methods Gaussian distribution Statistical fault detection Manufacturing industries Correlated variables Manufacturing processes Fault detection Control charts Semiconductor device manufacture K-Nearest Neighbors rule
Content Type	Text
Resource Type	Article

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