XXI Scientific Conference on Vibroacoustics and Vibrotechnics

On may 29-30, 2023, the DMC team took part in the XXI Scientific Conference on Vibroacoustics and Vibrotechnics organized in Pruszków-Warszawa by Faculty of Automotive and Construction Machinery Engineering from Warsaw University of Technology, Faculty of Mechanical Engineering and Robotics from AGH and The Polish Association of Technical Diagnostics. Among other great scientists from all over the Poland, DMC team has presented some of their recent work.

The highlight in form of a presentation from prof. Radosław Zimroz and prof. Agnieszka Wyłomańska took place at the end of the first day of conference. Presentation called “Diagnostics and forecasting of damage development under non-Gaussian interference conditions” was a general cross-section of the team’s achievements in the field of processing non-Gaussian signals.

For the conference closing session, DMC team prepared and presented six posters connected by the topic of machine diagnostics:

> “The use of dependence measures for the selection of the information frequency band in the diagnosis of local damage in the machine, for signals with non-Gaussian, heavy-tailed noise” by Justyna Hebda-Sobkowicz, Radosław Zimroz and Agnieszka Wyłomańska,

> “Fault detection in the presence of non-Gaussian noise using the analysis of probability distributions applied to time-frequency maps” by Anita Drewnicka, Anna Michalak, Radosław Zimroz and Agnieszka Wyłomańska,

> “Multidimensional data decomposition techniques in vibration technical diagnostics” by Jacek Wodecki, Anna Michalak and Radosław Zimroz,

> “Vision methods in monitoring the technical condition of mining machines” by Przemysław Dąbek and Pavlo Krot,

> “Modal analysis in monitoring the technical condition of industrial machines” by Pavlo Krot and Hamid Shiri,

> “Analysis of the statistical distribution of torque in the diagnosis of backlash in machine drive systems” by Paweł Zimroz and Pavlo Krot,

> “Robust switching Kalman filter for diagnostics of long-term condition monitoring data in the presence of non-Gaussian noise” by Hamid Shiri and Paweł Zimroz.