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

Year, pages: 2014, 387 - 409

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.

Vassileva, S., Doukovska, L., Sgurev, V. 2014. AI-Based Diagnostics for Fault Detection and Isolation in Process Equipment Service. In Computing and Informatics, vol. 33, no.2, pp. 387-409. 1335-9150.

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. 1335-9150.