Application for analysis of the multiple coherence function in diagnostic signal separation processes

• Autorzy: Wądołowski Mariusz , Pankiewicz Jarosław , Markuszewski Damian

Identyfikatory

DOI

10.21008/j.0860-6897.2020.3.24

• Języki publikacji: EN

Abstrakty

EN

Diagnosing the condition of the machine during its operation by non-invasive methods is most often reduced to measuring the acceleration of vibrations occurring on the housing, as close as possible to the observed element or changes in sound pressure in the immediate vicinity of the machine. For proper inference about the condition of a given machine element, the registered signals should be undisturbed by signals coming from other components and free from external interference. In the case of simple stationary machines, it is quite simple, but in the case of more complex systems, such as a car, which in addition is in motion, things get complicated In the available literature we find examples of the effectiveness of using ordinary coherence function to separate signals from two independent sources[1,2,3]. This work presents attempt to build an algorithm that uses signals from a multi-point measurement system to analyze multiple coherence functions, which allows to separate signals from various sources. It can then get diagnostic information from the signal thus separated. The effectiveness of the algorithm was tested on a model simulating signal mixing, and then using signal coherence function and knowledge of the transmittance function, the signals were separated.

Słowa kluczowe

EN

partial coherence; signal separation; machine diagnostics

PL

koherencja częściowa; separacja sygnałów; diagnostyka maszyn

• Wydawca: Poznan University of Technology. Institute of Applied Mechanics
• Czasopismo: Vibrations in Physical Systems
• Rocznik: 2020
• Tom: Vol. 31, nr 3
• Strony: art. no. 2020324
• Opis fizyczny: Bibliogr. 11 poz., rys., wykr.

Twórcy

autor

Wądołowski Mariusz

• Warsaw University of Technology, Faculty of Automotive and Construction Machinery Engineering, Institute of Machine Design Fundamentals, L. Narbutta 84, 02-524 Warsaw

autor

Pankiewicz Jarosław

• Warsaw University of Technology, Faculty of Automotive and Construction Machinery Engineering, Institute of Machine Design Fundamentals, L. Narbutta 84, 02-524 Warsaw

autor

Markuszewski Damian

• Warsaw University of Technology, Faculty of Automotive and Construction Machinery Engineering, Institute of Machine Design Fundamentals, L. Narbutta 84, 02-524 Warsaw

Bibliografia

• 1. J. Dziurdź, Application of correlation and coherence functions in diagnostic systems, Solid State Phenomena, 196 (2013) 3-12.
• 2. Z. Dabrowski, J. Dziurdz, D. Górnicka, Utilisation of the Coherence Analysis in Acoustic Diagnostics of Internal Combustion Engines, Archives of Acoustics, 42 (2017) 475-481.
• 3. J. Dziurdź, Separacja składowych widmowych w zadaniu identyfikacji modelu nieliniowego, Diagnostyka, 30 (2004) 162-166.
• 4. M. R. Reksoprodjo, W. Nirbito, Characteristics of Vibration Propagation on Passenger Car Monocoque Body Structure at Static Small Turbocharged Diesel Engine Speed Variation, Journal of Physics: Conference Series., 1519 (2020) 012005.
• 5. Z. Zhang, D. Pan W. Wu, C. Huang, Vibration source identification of a heavy commercial vehicle cab based on operational transfer path analysis, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 234 (2020) 669-680.
• 6. Y. Pan, Y. Li, M. Huang, Y. Liao, D. Liang, Noise source identification and transmission path optimisation for noise reduction of an axial piston pump, Applied Acoustics, 130 (2018) 283-292.
• 7. R. Szupiluk, P. Rubach, Metodyka i praktyka filtracji opartej na ślepej separacji sygnałów, Roczniki Kolegium Analiz Ekonomicznych/Szkoła Główna Handlowa, 2019, 183-195.
• 8. J.S. Bendat, A. G. Piersol, Random data: analysis and measurement procedures, John Wiley & Sons, 2011.
• 9. F. Berlato, G. D'elia, M. Battarra, G. Dalpiaz, Condition monitoring indicators for pitting detection in planetary gear units, Diagnostyka 21 (2020).
• 10. A. Taghizadeh-Alisaraei, A. Mahdavian, Fault detection of injectors in diesel engines using vibration time-frequency analysis, Applied Acoustics, 143 (2019) 48-58.
• 11. B.J. Krężel, P. Białkowski, Diagnostic of shock absorbers during road test with the use of vibration fft and cross-spectrum analysis, Diagnostyka, 18 (2017) 79-86.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).