Tomasz Barszcz

Professor

AGH University of Science and Technology

Poland

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About

Professor Tomasz Barszcz is a faculty member at AGH University of Science and Technology in Poland. His research focuses on advanced methodologies for automated monitoring and condition assessment in industrial systems, particularly using data-driven modeling and machine learning techniques. Recent publications demonstrate expertise in anomaly detection, image analytics, and the application of Hidden Markov Models in vibration analysis and diagnostics.

Articles (16)

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2025

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2025

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2024

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2024

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2024

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2023

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2022

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2022

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2022

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2022

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2022

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2022

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2022

Collaborators (5)

Phong Ba Dao

Associate Professor

AGH University of Science and Technology

POLAND

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Kajetan Dziedziech

Associate Professor

AGH University of Krakow

POLAND

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Anna Michalak

Wrocław University of Scince and Technology

POLAND

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Igor Wojnicki

AGH University of Science and Technology

POLAND

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Radoslaw Zimroz

Head/Professor

Wroclaw University of Science and Technology

POLAND

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Tomasz Barszcz

Research Interests

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Articles (16)

Heterogeneous Information Fusion for Robot-Based Automated Monitoring of Bearings in Harsh Environments via Ensemble of Classifiers with Dynamic Weighted Voting

Data‑driven modeling of staged planetary gearbox vibration signature

Anomaly detection of wind turbines based on stationarity analysis of SCADA data

Prediction of machine state for non-Gaussian degradation model using Hidden Markov Model approach

Advanced image analytics for mobile robot-based condition monitoring in hazardous environments: a comprehensive thermal defect processing framework

Semantic segmentation of thermal defects in belt conveyor idlers using thermal image augmentation and U‑Net‑based convolutional neural networks

Threshold lines identification for non-Gaussian distributed diagnostic features

Machine condition change detection based on data segmentation using a three-regime, α-stable Hidden Markov Model

Assessment of background noise properties in time and time–frequency domains in the context of vibration-based local damage detection in real environment

Automated Identification of Overheated Belt Conveyor Idlers in Thermal Images with Complex Backgrounds Using Binary Classification with CNN

Design of an Infrared Image Processing Pipeline for Robotic Inspection of Conveyor Systems in Opencast Mining Sites

Design of an Infrared Image Processing Pipeline for Robotic Inspection of Conveyor Systems in Opencast Mining Sites

Proposal of Multidimensional Data Driven Decomposition Method for Fault Identification of Large Turbomachinery

Proposal of Multidimensional Data Driven Decomposition Method for Fault Identification of Large Turbomachinery

A new approach to risk management in the power industry based on systems theory

Automatic identification of malfunctions of large turbomachinery during transient states with genetic algorithm optimization

Collaborators (5)

Phong Ba Dao

Kajetan Dziedziech

Anna Michalak

Igor Wojnicki

Radoslaw Zimroz

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