This paper presents a critical and analytical description of an ongoing research program aimed at the implementation of an expert system capable of monitoring, through an Intelligent Health Control procedure, the instantaneous performance of a cogeneration plant. An application has been tested on a real plant, located on the grounds of the ENEA-Casaccia Energy Laboratories. The expert system, denominated PROMISE as the Italian acronym for PROgnostic and Intelligent Monitoring Expert System, generates, in real time and in a form directly useful to the plant manager, information on the existence and severity of faults, forecasts on the future time history of both detected and likely faults, and suggestions on how to control the problem. The expert procedure, working where and if necessary with the support of a process simulator, derives from real-time data a list of selected performance indicators for each plant component. For a set of faults, pre-defined with the help of the plant operator, proper rules are defined in order to establish whether the component is working correctly; in several instances, since one single failure (symptom) can originate from more than one fault (cause), complex sets of rules expressing the combination of multiple indices have been introduced in the knowledge base as well. Creeping faults are detected by analyzing the trend of the variation of an indicator in a pre-assigned interval of time. Whenever the value of this "discrete time derivative" becomes "high" with respect to a specified limit value, a "latent creeping fault" condition is prognosed. The expert system architecture is based on an object-oriented paradigm. The knowledge base (facts and rules) is clustered: the chunks of knowledge pertain to individual components. A graphic user interface (GUI) allows the user to interrogate PROMISE about its rules, procedures, classes and objects, and about its inference path. The paper also presents the results of some tests conducted on the real plant. © 2004 Elsevier Ltd. All rights reserved.
Automatic diagnostics and prognostics of energy conversion processes via knowledge-based systems / T., Biagetti; Sciubba, Enrico. - In: ENERGY. - ISSN 0360-5442. - 29:12-15 SPEC. ISS.(2004), pp. 2553-2572. [10.1016/j.energy.2004.03.031]
Automatic diagnostics and prognostics of energy conversion processes via knowledge-based systems
SCIUBBA, Enrico
2004
Abstract
This paper presents a critical and analytical description of an ongoing research program aimed at the implementation of an expert system capable of monitoring, through an Intelligent Health Control procedure, the instantaneous performance of a cogeneration plant. An application has been tested on a real plant, located on the grounds of the ENEA-Casaccia Energy Laboratories. The expert system, denominated PROMISE as the Italian acronym for PROgnostic and Intelligent Monitoring Expert System, generates, in real time and in a form directly useful to the plant manager, information on the existence and severity of faults, forecasts on the future time history of both detected and likely faults, and suggestions on how to control the problem. The expert procedure, working where and if necessary with the support of a process simulator, derives from real-time data a list of selected performance indicators for each plant component. For a set of faults, pre-defined with the help of the plant operator, proper rules are defined in order to establish whether the component is working correctly; in several instances, since one single failure (symptom) can originate from more than one fault (cause), complex sets of rules expressing the combination of multiple indices have been introduced in the knowledge base as well. Creeping faults are detected by analyzing the trend of the variation of an indicator in a pre-assigned interval of time. Whenever the value of this "discrete time derivative" becomes "high" with respect to a specified limit value, a "latent creeping fault" condition is prognosed. The expert system architecture is based on an object-oriented paradigm. The knowledge base (facts and rules) is clustered: the chunks of knowledge pertain to individual components. A graphic user interface (GUI) allows the user to interrogate PROMISE about its rules, procedures, classes and objects, and about its inference path. The paper also presents the results of some tests conducted on the real plant. © 2004 Elsevier Ltd. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
