Skip to main content

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)


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.


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

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-47955-2_2
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   64.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-47955-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   84.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.


  1. Hugin expert, hugin expert A/S. Aalborg, Denmark (2000)

    Google Scholar 

  2. 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

    Google Scholar 

  3. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. Technical report TR 07-107, University of Minnesota, USA (2007)

    Google Scholar 

  4. Chickering, D.M.: Optimal structure identification with greedy search. J. Mach. Learn. Res. 3, 507–554 (2002)

    MathSciNet  MATH  Google Scholar 

  5. 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)

    CrossRef  MATH  Google Scholar 

  6. Ibargüengoytia, P.H., Sucar, L.E., Vadera, S.: Real time intelligent sensor validation. IEEE Trans. Power Syst. 16(4), 770–775 (2001)

    CrossRef  Google Scholar 

  7. Ibargüengoytia, P.H., Vadera, S., Sucar, L.E.: A probabilistic model for information and sensor validation. Comput. J. 49(1), 113–126 (2006)

    CrossRef  Google Scholar 

  8. 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)

    CrossRef  Google Scholar 

  9. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)

    MATH  Google Scholar 

  10. 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)

    CrossRef  Google Scholar 

  11. 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)

    Google Scholar 

Download references


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.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Pablo H. Ibargüengoytia .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

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.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47954-5

  • Online ISBN: 978-3-319-47955-2

  • eBook Packages: Computer ScienceComputer Science (R0)