loading
Papers

Research.Publish.Connect.

Paper

Authors: Khaoula Tidriri ; Nizar Chatti ; Sylvain Verron and Teodor Tiplica

Affiliation: 2 Avenue Notre Dame Du Lac, 49000 Angers and France

ISBN: 978-989-758-380-3

Keyword(s): Process Monitoring, Model-based Fault Diagnosis, Analytical Redundancy, Complex Systems.

Related Ontology Subjects/Areas/Topics: Engineering Applications ; Industrial Engineering ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Robotics and Automation ; Signal Processing, Sensors, Systems Modeling and Control ; Surveillance ; Systems Modeling and Simulation

Abstract: Fault Detection and Diagnosis (FDD) approaches have become increasingly important due to the growing demand for reliability and safety for modern systems. During the last decades, many works were reported about FDD approaches, especially model-based ones. The latter relies solely on a developed model that accurately describes the system, without exploiting any additional available data. In this work, we intent to make use of the physical model as well as historical data, for both normal operating state and faulty states. Hence, the paper focuses on the validation of an experimental approach, called Fault Training Analysis, that analyzes and identifies the causal relations between residuals and faults identified and observed on the system, by dealing with real measurement data from nominal and faulty states. It results on an experimental matrix, called Fault Training Matrix, that enhances the theoretical Fault Signature Matrix. The effectiveness of the proposed approach is validated th rough the challenging Tennessee Eastman Process. The application results on a high fault detection rate, a high fault diagnosis rate and a small false alarm rate. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.233.219.101

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Tidriri, K.; Chatti, N.; Verron, S. and Tiplica, T. (2019). Fault Training Matrix for Process Monitoring based on Structured Residuals.In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-380-3, pages 23-30. DOI: 10.5220/0007795600230030

@conference{icinco19,
author={Khaoula Tidriri. and Nizar Chatti. and Sylvain Verron. and Teodor Tiplica.},
title={Fault Training Matrix for Process Monitoring based on Structured Residuals},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2019},
pages={23-30},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007795600230030},
isbn={978-989-758-380-3},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Fault Training Matrix for Process Monitoring based on Structured Residuals
SN - 978-989-758-380-3
AU - Tidriri, K.
AU - Chatti, N.
AU - Verron, S.
AU - Tiplica, T.
PY - 2019
SP - 23
EP - 30
DO - 10.5220/0007795600230030

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.