AI-based Diagnostics for Fault Detection and Isolation in Process Equipment Service
Keywords:Process equipment service, fault detection and isolation, residuals, artificial intelligence, bio-ethanol production
AbstractRecent 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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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 https://www.cai.sk/ojs/index.php/cai/article/view/849