Fault Training Matrix for Process Monitoring based on Structured Residuals

Khaoula Tidriri, Nizar Chatti, Sylvain Verron, Teodor Tiplica

2019

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 through 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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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