Infrastructure Diagnosed by Solar Power Supply in an Intelligent Diagnostic System in Five-Valued Logic

Stanisław Duer, Marek Woźniak (), Jacek Paś, Marek Stawowy, Krzysztof Rokosz, Dariusz Bernatowicz, Radosław Duer and Atif Iqbal
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Stanisław Duer: Department of Energy, Faculty of Mechanical Engineering and Power Engineering, Technical University of Koszalin, 15-17 Raclawicka St., 75-620 Koszalin, Poland
Marek Woźniak: Doctoral School, Technical University of Koszalin, 2 Sniadeckich St., 75-620 Koszalin, Poland
Jacek Paś: Faculty of Electronic, Military University of Technology of Warsaw, 2 Urbanowicza St., 00-908 Warsaw, Poland
Marek Stawowy: Department of Transport Telecommunication, Faculty of Transport, Warsaw University of Technology, Koszykowa St. 75, 00-662 Warsaw, Poland
Krzysztof Rokosz: Faculty of Electronic and Informatics, Technical University of Koszalin, 2 Sniadeckich St., 75-620 Koszalin, Poland
Dariusz Bernatowicz: Faculty of Electronic and Informatics, Technical University of Koszalin, 2 Sniadeckich St., 75-620 Koszalin, Poland
Radosław Duer: Independent Researcher, 75-620 Koszalin, Poland
Atif Iqbal: School of Mechanical Engineering, Hangzhou City University, Hangzhou 310015, China

Energies, 2024, vol. 17, issue 10, 1-19

Abstract: This article discusses the issue of diagnosing low-power solar power plants using the five-valued (5VL) state evaluation {4, 3, 2, 1, 0}. We address in depth how the 5VL diagnostics built upon 2VL, 3VL, and 4VL—two-valued diagnostics, three-valued logistics, and four-valued diagnostics. Logic (5VL) assigns five state values to the range of signal value changes, and these states are completely operational ({4}), incomplete ({3}), critical efficiency ({2}), and pre-fault efficiency ({1}). For the identical ranges of diagnostic signal values, all three of the applied state valence logics interpret failure as changes outside of their permitted ranges. Diagnostic procedures made use of an AI-based DIAG 2 system. This article’s goal is to provide a comprehensive overview of the DIAG 2 intelligent diagnostic system, including its architecture, algorithm, and inference rules. Diagnosis with the DIAG 2 system is based on a well-established technique for comparing diagnostic signal vectors with reference signal vectors. A differential vector metric is born out of this examination of vectors. The input cells of the neural network implement the challenge of signal analysis and comparison. It is then possible to classify the object components’ states in the neural network’s output cells. Based on the condition of the object’s constituent parts, this approach can signal whether those parts are working, broken, or urgently require replacement.

Keywords: neural networks; intelligent systems; servicing process; diagnostic process; expert system; knowledge base; low-power solar plant devices; diagnostic information (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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