Sensor fault detection based on principal component analysis for interval-valued data

Principal component analysis (PCA)-based fault detection and isolation (FDI) is a well-established data-driven diagnosis strategy that has long been praised for its performances. However, it is still not optimal for uncertain systems, mainly since the model uncertainties usually have a significant e...

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Bibliographic Details
Published in:Quality engineering 2018-10, Vol.30 (4), p.635-647
Main Authors: Ait-Izem, Tarek, Harkat, M.-Faouzi, Djeghaba, Messaoud, Kratz, Frédéric
Format: Article
Language:English
Subjects:
complete-information PCA
Computer simulation
Error detection
Fault detection
Foreign investment
interval-valued data
Milling (machining)
milling machine
Milling machines
Modelling
principal component analysis
Principal components analysis
SPE statistic
Uncertainty
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Summary:Principal component analysis (PCA)-based fault detection and isolation (FDI) is a well-established data-driven diagnosis strategy that has long been praised for its performances. However, it is still not optimal for uncertain systems, mainly since the model uncertainties usually have a significant effect on the reliability of the method. As an alternative solution, modeling with PCA for interval-valued data ensures a better monitoring by apprehending the sensor uncertainties and including them in the modeling phase. This article presents an extension of data-driven PCA fault detection to the case of interval-valued data. The PCA model is built based on the complete information principal component analysis (CIPCA) for interval-valued data, and different fault detection indices are generated based on the squared prediction error (SPE) statistic. A fault detection scheme is proposed based on squared interval norm of residuals vector. The performances of the proposed fault detection scheme are illustrated using a simulation example and a milling machine process, along with a Monte Carlo experiment for validation.
ISSN:0898-2112
1532-4222
DOI:10.1080/08982112.2017.1391288