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)

1
  • Advanced testing of heavy duty gearboxes in non-stationary operational conditions
2
  • Application of angular-temporal spectrum for detection of rolling-element bearing faults operating under varying speed regime
3
  • ART-2 artificial neural networks applications for classification of vibration signals and operational states of wind turbines for intelligent monitoring
4
  • Automatic and full-band demodulation for fault detection – validation on a wind turbine test rig
5
  • Blind separation of vibration components for rotor machinery operating under highly non-stationary regime
6
7
  • Hybrid system of ART and RBF neural networks for classification of vibration signals and operational states of wind turbines
8
9
  • Joint power-speed representation of vibration features
10
  • Modeling of rotating machine vibration signals operating under highly varying speed and load
11
  • Non-clustering method for autmatic selection of machine operational states
12
  • Poprawność działania układy pomiarowego
13
  • Stability of power grids with significant share of wind farms
14
  • Triple correlation as a diagnostic tool for rotating machinery
15
  • Vertical axis wind turbine states classification by an ART-2 neural network with a stereographic projection as a signal normalization
16
  • Vibration velocity RMS in online monitoring systems – frequency spectra based algorithm approach
17
  • Wavelet-based variance analysis for condition monitoring of gears