Paweł Pawlik

Associate Professor

AGH University of Science and Technology

Poland

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About

Dr. Paweł Pawlik is an Associate Professor at AGH University of Science and Technology in Poland. His research focuses on fault diagnosis in rotating machinery, machine learning applications for condition monitoring, and the assessment of infrasound and low-frequency noise. Recent publications include studies on the Naive Bayes Classifier for monitoring systems, auto-encoder approaches for gearbox fault identification, and the effects of wind turbine infrasound on human mental performance.

Articles (15)

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2025

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2022

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2022

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2021

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2021

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2020

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2020

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2020

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2020

Collaborators (6)

Tadeusz Wszolek

AGH University of Science and Technology

POLAND

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Bartosz Przysucha

Lublin University of Technology

POLAND

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Adam Dudarewicz

Nofer Institute of Occupational Medicine

POLAND

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Malgorzata Pawlaczyk-Luszczynska

Nofer Institute of Occupational Medicine

POLAND

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Dominik Mleczko

AGH University of Science and Technology

POLAND

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Bartłomiej Stępień

profesor uczelni

AGH University of Krakow

POLAND

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Paweł Pawlik

Research Interests

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About

Articles (15)

The use of a Naive Bayes Classifier in continuous monitoring systems for rotating machinery

Gearbox fault identification using auto-encoder without training data from the damaged machine

Suitability analysis of selected methods for modelling infrasound and low-frequency noise from wind turbines

CMAFI — Copula-based Multifeature Autocorrelation Fault Identification of rolling bearing

Does Stochastic and Modulated Wind Turbine Infrasound Affect Human Mental Performance Compared to Steady Signals without Modulation? Results of a Pilot Study

Fault diagnosis of machines operating in variable conditions using artificial neural network not requiring training data from a faulty machine

Vibroacoustic method of diagnosing rotating machines operating under variable load

Experimental Verification of Windshields in the Measurement of Low Frequency Noise from Wind Turbines

Experimental verification of the usefulness of selected infrasound and low-frequency noise (ILFN) indicators in assessing the noise annoyance of wind turbines

The Use of Deep Learning Methods in Diagnosing Rotating Machines Operating in Variable Conditions

Impact of the noise indicators components correlation L<inf>d</inf>,L<inf>e</inf>,L<inf>n</inf> on the uncertainty of the long-term day–evening–night noise indicator L<inf>den</inf>

Analysis of the impact of oil viscosity on the vibroacoustic signal of a planetary gearbox operating at variable load

Probability distributions of one-day noise indicators in the process of the type A uncertainty evaluation of long-term noise indicators

Analysis of multi-source data for monitoring and control of intelligent technological systems,(Analiza danych wieloźródłowych do monitorowania i kontroli inteligentnych systemów technologicznych)

Analysis of the Usefulness of Measurement on a Board at Ground Level for Assessing the Noise Level from a Wind Turbine

Collaborators (6)

Tadeusz Wszolek

Bartosz Przysucha

Adam Dudarewicz

Malgorzata Pawlaczyk-Luszczynska

Dominik Mleczko

Bartłomiej Stępień

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