LEARNING DISCRETE PROBABILISTIC MODELS FOR APPLICATION IN MULTIPLE FAULTS DETECTION

Luis E. Garza Castañón, Francisco J. Cantú Ortíz, Rubén Morales-Menéndez

Abstract

We present a framework to detect faults in processes or systems based on probabilistic discrete models learned from data. Our work is based on a residual generation scheme, where the prediction of a model for process normal behavior is compared against measured process values. The residuals may indicate the presence of a fault. The model consists of a general statistical inference engine operating on discrete spaces, and represents the maximum entropy joint probability mass function (pmf) consistent with arbitrary lower order probabilities. The joint pmf is a rich model that, once learned, allows us to address inference tasks, which can be used for prediction applications. In our case the model allows the one step-ahead prediction of process variable, given its past values. The relevant dependencies between the forecast variable and past values are learnt by applying an algorithm to discover discrete bayesian network structures from data. The parameters of the statistical engine are also learn by an approximate method proposed by Yan and Miller. We show the performance of the prediction models and their application in power systems fault detection.

References

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Paper Citation


in Harvard Style

E. Garza Castañón L., J. Cantú Ortíz F. and Morales-Menéndez R. (2008). LEARNING DISCRETE PROBABILISTIC MODELS FOR APPLICATION IN MULTIPLE FAULTS DETECTION . In Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8111-30-2, pages 187-192. DOI: 10.5220/0001491801870192


in Bibtex Style

@conference{icinco08,
author={Luis E. Garza Castañón and Francisco J. Cantú Ortíz and Rubén Morales-Menéndez},
title={LEARNING DISCRETE PROBABILISTIC MODELS FOR APPLICATION IN MULTIPLE FAULTS DETECTION},
booktitle={Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2008},
pages={187-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001491801870192},
isbn={978-989-8111-30-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - LEARNING DISCRETE PROBABILISTIC MODELS FOR APPLICATION IN MULTIPLE FAULTS DETECTION
SN - 978-989-8111-30-2
AU - E. Garza Castañón L.
AU - J. Cantú Ortíz F.
AU - Morales-Menéndez R.
PY - 2008
SP - 187
EP - 192
DO - 10.5220/0001491801870192