# LEARNING DISCRETE PROBABILISTIC MODELS FOR APPLICATION IN MULTIPLE FAULTS DETECTION

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

#### 2008

#### 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

- Chen, D. and Zhang, J. (2005). Time series prediction based on ensemble anfis. In Proceedings of the fourth International Conference on Machine Learning and Cybernetics. IEEE.
- Davy, M., Desorbry, F., Gretton, A., and Doncarli, C. (2006). An online support vector machine for abnormal events detection. In Signal Processing 86 (2006). Elsevier.
- Frank, P. (1990). Fault diagnosis in dynamic systems unisg analytical and knowledge based redundancy a survey and new results. In Automatica. Elsevier.
- Gentil, S., Montmain, J., and Combastel, C. (2004). Combining fdi and ai approaches within causal-modelbased diagnosis. In IEEE Transactions on Systems, Man and Cybernetics, part B. IEEE.
- Grainger, W. and Stevenson, W. (1994). Power Systems Analysis. McGraw-Hill, USA.
- Luque, C., Valss, J., and Isasi, P. (2007). Time series forecasting by means of evolutionary algorithms. In Proceedings of the Parallel and Distributed Processing Symposium 2007. IEEE.
- Ma, Q., Zheng, Q., Peng, H., Zhong, T., and Xu, L. (2007). Chaotic time series prediction based on evolving recurrent neural networks. In Proceedings of the fourth International Conference on Machine Learning and Cybernetics. IEEE.
- Matsuura, J. P. and Yoneyama, T. (2004). Learning bayesian networks for fault detection. In International Workshop on Machine Learning for Signal Processing. IEEE.
- Sedighi, A., Haghifam, M., and Malik, O. (2005). Soft computing applications in high impedance fault detection in distribution systems. In Electric Power Systems Research 76 (2005). Elsevier.
- Vanajakshi, L. and Rilett, L. (2007). Support vector machine technique for the short term prediction of travel time. In Proceedings of the 2007 Intelligent Vehicles Symposium. IEEE.
- Venkatasubramanian, V., Rengaswamy, R., k. Yin, and Kavuri, S. (2003). A review of process fault detection and diagnosis part 1, part 2 and part 3. In Computers and Chemical Engineering. Elsevier.
- Wang, L. (1997). A Course in Fuzzy Systems and Control. Prentice Hall, USA.
- Xu, L. and Chow, M. (2005). Power distribution systems fault case identification using logistic regression and artificial neural network. In Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems.
- Yongli, Z., Limin, H., and Jinling, L. (2006). Bayesian networks-based approach for power systems fault diagnosis. In IEEE Transactions on Power Delivery. IEEE.
- Zhang, P. and Ding, S. X. (2005). A simple fault detection scheme for nonlinear systems. In Proceedings of the 2005 IEEE International Symposium on Intelligent Control. IEEE.

#### 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