AI-based Diagnostics for Fault Detection and Isolation in Process Equipment Service

Authors

  • Svetla Vassileva Institute of System Engineering and Robotics, Bulgarian Academy of Sciences, Acad. G. Bonchev str., bl. 2, 1113 Sofia
  • Lyubka Doukovska Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev str., bl. 2, 1113 Sofia
  • Vassil Sgurev Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev str., bl. 2, 1113 Sofia

Keywords:

Process equipment service, fault detection and isolation, residuals, artificial intelligence, bio-ethanol production

Abstract

Recent industry requires efficient fault discovering and isolation solutions in process equipment service. This problem is a real-world problem of typically ill-defined systems, hard to model, with large-scale solution spaces. Design of precise models is impractical, too expensive, or often non-existent. Support service of equipment requires generating models that can analyze the equipment data, interpreting the past behavior and predicting the future one. These problems pose a challenge to traditional modeling techniques and represent a great opportunity for the application of AI-based methodologies, which enable us to deal with imprecise, uncertain data and incomplete domain knowledge typically encountered in real-world applications. In this paper the state of the art, theoretical background of conventional and AI-based techniques in support of service tasks and illustration of some applications to process equipment service on bio-ethanol production process are shortly described.

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Author Biography

Lyubka Doukovska, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev str., bl. 2, 1113 Sofia

Institute of Information and Communication Technologies, Head of Intelligent Systems Department

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Published

2014-06-27

How to Cite

Vassileva, S., Doukovska, L., & Sgurev, V. (2014). AI-based Diagnostics for Fault Detection and Isolation in Process Equipment Service. Computing and Informatics, 33(2), 387–409. Retrieved from http://147.213.75.17/ojs/index.php/cai/article/view/849