Wykaz publikacji wybranego autora

Tomasz Barszcz, prof. dr hab. inż.

profesor zwyczajny

Wydział Inżynierii Mechanicznej i Robotyki
WIMiR-krm, Katedra Robotyki i Mechatroniki


  • 2018

    [dyscyplina 1] dziedzina nauk inżynieryjno-technicznych / inżynieria mechaniczna


[poprzednia klasyfikacja] obszar nauk technicznych / dziedzina nauk technicznych / automatyka i robotyka


Identyfikatory Autora Informacje o Autorze w systemach zewnętrznych

ORCID: 0000-0002-1656-4930 orcid iD

ResearcherID: HHS-2538-2022

Scopus: 25521161400

PBN: 5e709208878c28a04738eece

OPI Nauka Polska

System Informacyjny AGH (SkOs)




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  • A new approach to risk management in the power industry based on systems theory
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  • Adjustment of a feedwater heater model in bi-stationary load conditions
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  • Advanced methods for condition monitoring of machinery in distributed online monitoring and diagnostic systems
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  • Advanced testing of heavy duty gearboxes in non-stationary operational conditions
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  • An example of diagnostics system based on OMA(X) method and NARX models for rotating machinery
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  • Analiza możliwości redukcji drgań maszyny wirnikowej średniej mocy
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  • Application of an open environment for simulation of power plant unit operation under steady and transient conditions
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  • Application of angular-temporal spectrum to exploratory analysis of generalized angular-temporal deterministic signals
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  • Application of artificial neural network for damage detection in planetary gearbox of wind turbine
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  • Application of diagnostic algorithms for wind turbines
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  • Application of Hardware-In-The-Loop for Virtual Power Plant
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  • Application of probabilistic neural networks for detection of mechanical faults in electric motors
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  • Application of probabilistic neural networks for fault detection in rotating machinery
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  • Application of vibration monitoring for mining machinery in varying operational conditions
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  • Application of virtual power plant for condition monitoring of power generation unit
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  • ART-2 artificial neural networks applications for classification of vibration signals and opera-tional states of wind turbines for intelligent monitoring