In this section
@inproceedings{Cecilio:2015,
author = {Cecilio, IM and Ottewill, JR and Thornhill, NF},
title = {Adapting nearest neighbors-based monitoring methods to irregularly sampled measurements},
url = {http://www.phmsociety.org/node/1746/},
year = {2015}
}
TY - CPAPER
AB - Prognostics and Health Management Society. All rights reserved.Irregularly spaced measurements are a common quality problem in real data and preclude the use of several feature extraction methods, which were developed for measurements with constant sampling intervals. Feature extraction methods based on nearest neighbors of embedded vectors are an example of such methods. This paper proposes the use of a timebased construction of embedded vectors and a weighted similarity metric within nearest neighbor-based methods in order to extend their applicability to irregularly sampled measurements. The proposed idea is demonstrated within a method of univariate detection of transient or spiky disturbances. The result obtained with an irregularly sampled measurement is benchmarked by the original regularly sampled measurement. Although the method was originally implemented for off-line analysis, the paper also discusses modifications to enable its on-line implementation.
AU - Cecilio,IM
AU - Ottewill,JR
AU - Thornhill,NF
PY - 2015///
TI - Adapting nearest neighbors-based monitoring methods to irregularly sampled measurements
UR - http://www.phmsociety.org/node/1746/
UR - http://hdl.handle.net/10044/1/45735
ER -
Nina Thornhill, ABB/RAEng Professor of Process Automation
Centre for Process Systems Engineering
Department of Chemical Engineering
Imperial College London
South Kensington Campus, London SW7 2AZ
Tel: +44 (0)20 7594 6622
Email: n.thornhill@imperial.ac.uk