Fault Detection and Diagnosis Approach based on Observers and SVD-PCA

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Abstract

In this paper, a new combined approach for fault detection and diagnosis (FDD) of abrupt additive actuator and sensor faults, based on Kalman observer (KFO), on sliding mode observers (SMO), on singular values decomposition (SVD), and on principal component analysis (PCA), is proposed. The main contribution is the combined approach proposed for FDD based on ratios between singular values of the adaptive slidingwindow SVD-PCA model and on an improved SMO observer that estimates the faults magnitude. In order to show the performance, simulation results with a DTS-200 benchmark linear model are presented.
Original languageEnglish
Title of host publicationProceedings of the 2015 IEEE International Conference on Industrial Technology
Place of PublicationSeville, Spain
PublisherIEEE
Pages246-251
Number of pages6
ISBN (Print)978-1-4799-7800-7
DOIs
Publication statusPublished - 2015
Event2015 IEEE International Conference on Industrial Technology, ICIT 2015 - Seville, Spain
Duration: 17 Mar 201519 Mar 2015

Conference

Conference2015 IEEE International Conference on Industrial Technology, ICIT 2015
CountrySpain
CitySeville
Period17/03/1519/03/15

Keywords

  • SLIDING MODE OBSERVERS

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  • Cite this

    Palma, L., Ferreira, B., Gil, P., & Coito, F. (2015). Fault Detection and Diagnosis Approach based on Observers and SVD-PCA. In Proceedings of the 2015 IEEE International Conference on Industrial Technology (pp. 246-251). [7125106] IEEE. https://doi.org/10.1109/ICIT.2015.7125106