Izvorni znanstveni članak
https://doi.org/10.17559/TV-20171230222050
Modelling of an Expert System for Diagnosing the Operational Status of a Diesel Genset
Dejan Barešić
orcid.org/0000-0003-1820-3036
; Croatian Military Academy "Dr. Franjo Tuđman", Ilica 256b, 10000 Zagreb, Croatia
Željko Hederić
orcid.org/0000-0001-7265-0932
; J. J. Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology, Ul. kneza Trpimira 2B, 31000 Osijek, Croatia
Miralem Hadžiselimović
; University of Maribor, Faculty of Energy Technology, Hočevarjev trg 1, 8270 Krško, Slovenia
Sažetak
The modelling of an expert system for diagnosing the operational status of a diesel genset represents the final stage of the research undertaken so far on military gensets. The research was undertaken in several stages on a large number of gensets, and the results obtained at specific research stages were published at scientific-expert conferences. For the purpose of modelling an expert system, a knowledge base was compiled based on detailed observation of the genset operational status, interviews with experts with many years of experience in maintaining military gensets and also on a breakdown of the occurred faults archived in the overhaul documentation.The paper uses only that one segment of the knowledge base that is necessary for modelling a simplified form of the Bayesian network for fault detection in assembled condition. In addition to input probabilities, the results of diagnostic tests and simulations carried out in the Matlab Simulink program package are also entered in the Bayesian network. Fault detection represents a complex process so the application of an expert system significantly reduces the time needed for fault detection, resulting in optimized maintenance. It is especially significant in military and similar organizations which apply a large number of technical resources. The Bayesian network is processed in the GeNIe program package.
Ključne riječi
diagnosing the operational status; expert system; fault detection; genset; maintenance optimization
Hrčak ID:
205941
URI
Datum izdavanja:
22.9.2018.
Posjeta: 1.287 *