Adam Jablonski

Prof. Adam Jablonski

AGH University of Science and Technology, Kraków, Małopolskie, Poland
orcid

Research Focus & Metrics

Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 July 2019 in Mechanical Systems and Signal Processing
Verified
Journal Article
Closed access
BZBacem Zghal
Bacem Zghal
OGOUSSAMA GRAJA
OUSSAMA GRAJA
KDKajetan Dziedziech
Kajetan Dziedziech
FCFakher Chaari
Fakher Chaari
AJAdam Jablonski
Adam Jablonski
MHMohamed Haddar
Mohamed Haddar

1 November 2018 in Measurement
Verified
Journal Article
Closed access
KDKajetan Dziedziech
Kajetan Dziedziech
AJAdam Jablonski
Adam Jablonski
ZDZiemowit Dworakowski
Ziemowit Dworakowski

18 February 2025 in Mechanical Systems and Signal Processing
Verified
Journal Article
Open access
AJAdam Jablonski
Adam Jablonski
KMKrzysztof Mendrok
Krzysztof Mendrok


19 May 2021 in Sensors
Verified
Journal Article
Open access
PDF file
JGJakub Górski
Jakub Górski
AJAdam Jablonski
Adam Jablonski
MHMateusz Heesch
Mateusz Heesch
MDMichal Dziendzikowski
Michal Dziendzikowski
ZDZiemowit Dworakowski
Ziemowit Dworakowski
Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.

1 April 2019 in Mechanical Systems and Signal Processing
Verified
Journal Article
Closed access
KDKajetan Dziedziech
Kajetan Dziedziech
AJAdam Jablonski
Adam Jablonski
ZDZiemowit Dworakowski
Ziemowit Dworakowski