Abstract
Behavior can be defined as combination of variable’s values according to external inputs or environmental changes. This definition can be applied to persons, equipment, social systems or industrial processes. This paper proposes a probabilistic mechanism to represent the behavior of industrial equipment and an algorithm to identify deviations to this behavior. The anomaly detection mechanisms, together with the sensor validation theory are combined to propose an efficient manner to diagnose industrial equipment. A case study is presented with the failure identification of a wind turbine. The diagnosis is conducted when detecting deviations to the turbine normal behavior.
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References
Hugin expert, hugin expert A/S. Aalborg, Denmark (2000)
Andersen, S.K., Olesen, K.G., Jensen, F.V., Jensen, F.: Hugin: a shell for building bayesian belief universes for expert systems. In: Proceedings of the Eleventh Joint Conference on Artificial Intelligence, IJCAI, pp. 1080–1085, Detroit, Michigan, USA, 20–25 August 1989
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. Technical report TR 07-107, University of Minnesota, USA (2007)
Chickering, D.M.: Optimal structure identification with greedy search. J. Mach. Learn. Res. 3, 507–554 (2002)
Frank, P.M.: Fault diagnosis in dynamic systems using analytical and knowledge based redundancy- a survey and some new results. Automatica 26, 459–470 (1990)
Ibargüengoytia, P.H., Sucar, L.E., Vadera, S.: Real time intelligent sensor validation. IEEE Trans. Power Syst. 16(4), 770–775 (2001)
Ibargüengoytia, P.H., Vadera, S., Sucar, L.E.: A probabilistic model for information and sensor validation. Comput. J. 49(1), 113–126 (2006)
Márquez, F.P.G., Tobias, A.M., Pérez, J.M.P., Papaelias, M.: Condition monitoring of wind turbines: techniques and methods. Renew. Energy 46, 169–178 (2012)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)
Schlechtingen, M., Santos, I.F., Achiche, S.: Wind turbine condition monitoring based on scada data using normal behavior models. Part 1: system description. Appl. Soft Comput. 13, 259–270 (2013)
Zhou, A., Yu, D., Zhang, W.: A research on intelligent fault diagnosis of wind turbines based on ontology and FMECA. Adv. Eng. Inform. 32, 255–270 (2014)
Acknowledgements
This work is a preliminary result of the P12 project of the Mexican Center of Innovation in Energy (CEMIE-Eólico), partially sponsored by Fund (FSE) CONACYT-SENER Energy Sustainability, and at the IIE, under the project 14629. Authors also thank the anonymous referees for their insightful comments.
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Ibargüengoytia, P.H., García, U.A., Reyes, A., Borunda, M. (2016). Anomalies Detection in the Behavior of Processes Using the Sensor Validation Theory. In: Montes y Gómez, M., Escalante, H., Segura, A., Murillo, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2016. IBERAMIA 2016. Lecture Notes in Computer Science(), vol 10022. Springer, Cham. https://doi.org/10.1007/978-3-319-47955-2_2
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DOI: https://doi.org/10.1007/978-3-319-47955-2_2
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