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
  • A novel transformation of non-stationary signals to periodic signals
2
  • A novel transformation of non-stationary signals to periodic signals
3
  • A sensor fusion system with thermal infrared camera and lidar for automatic detection and localization of overheated idlers on conveyor systems
4
  • Acquisition of vibration signals in highly-varying environment
5
  • Advanced testing of heavy duty gearboxes in non-stationary operational conditions
6
  • AIDA 951® – Turbosets monitoring and diagnostics system
7
  • An example of application of model-based approach to SHM of rotating machinery
8
  • Analiza stanu dynamicznego maszyn wirnikowych na przykładzie stanowiska laboratoryjnego
9
  • Analysis of Fast Kurtogram performance in case of high level non-Gaussian noise
10
  • Application of angular-temporal spectrum for detection of rolling-element bearing faults operating under varying speed regime
11
  • Application of CNN in binary classification of thermal images with complex background for identification of overheated belt conveyor idlers
12
  • Application of diagnostic algorithm for wind turbines
13
  • Application of hardware-in-the-loop for virtual power plant
14
  • Application of instantaneous power spectrum for diagnostics of machinery operating under non-stationary load condition
15
  • Application of modular diagnostics system in power generation industry
16
  • Application of spectral kurtosis for diagnostics of machinary
17
  • ART-2 artificial neural networks applications for classification of vibration signals and operational states of wind turbines for intelligent monitoring
18
  • Automatic adjustment of signal amplitudes with respect to sensor placement
19
  • Automatic and full-band demodulation for fault detection – validation on a wind turbine test rig
20
  • Bearings fault detection in gas compressor with high impulsive noise level
21
  • Blind estimation of instantaneous rotational speed
22
  • Blind separation of vibration components for rotor machinery operating under highly non-stationary regime
23
  • Challenges in application of MEMS sensors for condition monitoring of machinery
24
  • Challenges in maintenance of the vibration monitoring systems dedicated to underground mining machinery
25
  • Comparison of advanced fault detection methods for rolling bearings fault in wind turbines