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Anomalies Detection in the Behavior of Processes Using the Sensor Validation Theory

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10022)

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.

Keywords

  • Anomaly detection
  • Model of behavior
  • Bayesian networks
  • Wind turbines

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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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Correspondence to Pablo H. Ibargüengoytia .

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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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