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.

Keywords

MSC

68T01

Cite this article

@article{cai-849,
  author  = {Svetla Vassileva and Lyubka Doukovska and Vassil Sgurev},
  title   = {AI-Based Diagnostics for Fault Detection and Isolation in Process Equipment Service},
  journal = {Computing and Informatics},
  volume  = {33},
  number  = {2},
  pages   = {387--409},
  year    = {2014},
  issn    = {1335-9150},
  url     = {https://cai.type.sk/content/2014/2/ai-based-diagnostics-for-fault-detection-and-isolation-in-process-equipment-service/}
}
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