A research activity has been initiated to study the developement of a diagnostic methodology, for the optimization of energy efficiency and the maximization of the operational time in those conditions, based on Artificial Intelligence (AI) techniques such as Artificial Neural Network (ANN) and fuzzy logic. The diagnostic procedure, developed specifically for the cogeneration plant located at the Engineering Department of the University of Perugia, must be characterized by a modular architecture to obtain a flexible architecture applicable to different systems. The first part of the study deals with the identifing the principal modules and the corresponding variables necessary to evaluate the module “health state”. Also the consequent upgrade of the monitoring system is described in this paper. Moreover it describes the structure proposed for the diagnostic procedure, consisting of a procedure for measurement validation and a fuzzy logic – based inference system. The first reveals the presence of abnormal conditions and localizes their source distinguishing between system failure and instrumentation malfunctions. The second provides an evaluation of module health state and the classification of the failures which have possibly occurred. The procedure was implemented in C++.
Design of the measurements validation procedure and the expert system architecture for a cogeneration internal combustion engine
BARELLI, Linda;BIDINI, Gianni
2005
Abstract
A research activity has been initiated to study the developement of a diagnostic methodology, for the optimization of energy efficiency and the maximization of the operational time in those conditions, based on Artificial Intelligence (AI) techniques such as Artificial Neural Network (ANN) and fuzzy logic. The diagnostic procedure, developed specifically for the cogeneration plant located at the Engineering Department of the University of Perugia, must be characterized by a modular architecture to obtain a flexible architecture applicable to different systems. The first part of the study deals with the identifing the principal modules and the corresponding variables necessary to evaluate the module “health state”. Also the consequent upgrade of the monitoring system is described in this paper. Moreover it describes the structure proposed for the diagnostic procedure, consisting of a procedure for measurement validation and a fuzzy logic – based inference system. The first reveals the presence of abnormal conditions and localizes their source distinguishing between system failure and instrumentation malfunctions. The second provides an evaluation of module health state and the classification of the failures which have possibly occurred. The procedure was implemented in C++.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
