Sensor fault detection based on principal component analysis for interval-valued data-Reference-Cited by-同舟云学术

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

Published:2017-12-08 Issue:4 Volume:30 Page:635-647
ISSN:0898-2112
Container-title:Quality Engineering
language:en
Short-container-title:Quality Engineering

Author:

Ait-Izem Tarek¹²^ORCID,Harkat M.-Faouzi³,Djeghaba Messaoud¹,Kratz Frédéric²

Affiliation:

1. Automatics and Signals Laboratory Annaba (LASA), Department of Electronics, Badji Mokhtar University, Annaba, Algeria

2. Laboratoire PRISME, INSA Centre Val-de-Loire, Bourges Cedex, France

3. Laboratory of Systems and Advanced Materials (LASMA), Badji Mokhtar University, Annaba, Algeria

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Link

https://www.tandfonline.com/doi/pdf/10.1080/08982112.2017.1391288

Reference24 articles.

1. Agogino, A., and K. Goebel. 2007. BEST lab, UC Berkeley. Milling Data Set, NASA Ames Prognostics Data Repository (http://ti.arc.nasa.gov/project/prognostic-data-repository), Moffett Field, CA: NASA Ames Research Center.

2. Ait-Izem, T., W. Bougheloum, M. F. Harkat, and M. Djeghaba. 2015a. Vertices and centers principal component analysis for fault detection and isolation. 2nd international conference on automation, control, engineering and computer science, Sousse, Tunisia, March 22–24, 2015.

3. Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in the One-Way Classification

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