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
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.
mathematics subject classification 2000: 68T01
reference: Vol. 33, 2014, No. 2, pp. 387–409