Automatic method for spectral pattern association with characteristic frequencies

• Autorzy: Firla M. , Gerber T. , Bellemain P. , Martin N.

Warianty tytułu

PL

Metoda automatycznego łączenia schematów spektralnych z charakterystycznymi częstotliwościami

• Języki publikacji: EN

Abstrakty

EN

This paper proposes an advanced signal-processing technique to improve the condition monitoring of rotating machinery. The proposed method employs the results of a blind spectrum interpretation including harmonic and sideband series detection. The contribution of this paper is an algorithm for automatic association of harmonic and sideband series with the characteristic fault frequencies listed in the kinematic configuration of the monitored system. The proposed algorithm is efficient in inspection of real-world signals, which contain a vast number of detected spectral components. The proposed approach has the advantage of taking into account a possible slip of the rolling-element bearings. The performance of the proposed algorithm is illustrated on real-world data by investigating a shaft problem of an industrial wind turbine high-speed shaft.

PL

W artykule zaproponowano zaawansowaną technikę przetwarzania sygnałów w celu poprawy monitorowania stanu maszyn wirujących. Przedstawiona metoda wykorzystuje wyniki ślepej interpretacji widma sygnału, m. in. detekcję serii harmonicznych i wstęg bocznych. Wkład zaprezentowany w tym artykule to algorytm do automatycznego łączenia serii harmonicznych oraz wstęg bocznych z charakterystycznymi częstotliwościami dostępnymi na podstawie konfiguracji kinematycznej monitorowanej maszyny. Zaproponowany algorytm jest skuteczny w badaniu sygnałów rzeczywistych, które zawierają dużą liczbę wykrytych elementów widmowych. Zaletą zaproponowanej metody jest uwzględnianie możliwego poślizgu łożyska tocznego. Działanie zaproponowanego algorytmu zostało zilustrowane na przykładzie rzeczywistych danych, który pokazuje problem wału wysokoobrotowego przemysłowej turbiny wiatrowej.

Słowa kluczowe

EN

condition monitoring; characteristic fault frequencies; wind shaft turbine; diagnosis

PL

monitorowanie stanu; częstotliwości charakterystyczne uszkodzeń; diagnostyka; wał; turbina wiatrowa

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2015
• Tom: Vol. 16, No. 4
• Strony: 77--84
• Opis fizyczny: Bibliogr. 21 poz., fot., rys., tab., wykr.

Twórcy

autor

Firla M.

• University of Technology, Faculty of Information Technology Univ. Grenoble Alpes, GIPSA-Lab, F-38000 Grenoble, France CNRS, GIPSA-Lab, F-38000 Grenoble, France

autor

Gerber T.

• University of Technology, Faculty of Information Technology Univ. Grenoble Alpes, GIPSA-Lab, F-38000 Grenoble, France CNRS, GIPSA-Lab, F-38000 Grenoble, France

autor

Bellemain P.

• University of Technology, Faculty of Information Technology Univ. Grenoble Alpes, GIPSA-Lab, F-38000 Grenoble, France CNRS, GIPSA-Lab, F-38000 Grenoble, France

autor

Martin N.

• University of Technology, Faculty of Information Technology Univ. Grenoble Alpes, GIPSA-Lab, F-38000 Grenoble, France CNRS, GIPSA-Lab, F-38000 Grenoble, France

Bibliografia

• [1] García Márquez F. P., Tobias A. M., Pinar Pérez J. M., Papaelias M.: Condition monitoring of wind turbines: Techniques and methods, Renewable Energy, 46 (2012) pp. 169-178.
• [2] Liu W., Tang B., Han J., Lu X., Hu N., He Z.: The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review, Renewable and Sustainable Energy Reviews, 44 (2015) pp. 466-472.
• [3] Agrawal K. K., Pandey P. G. N., Chandrasekaran K.: Analysis of the Condition Based Monitoring System for Heavy Industrial Machineries, Computational Intelligence and Computing Research (ICCIC), 2013 IEEE International Conference.
• [4] Tian Z., Jin T.: Maintenance of wind turbine systems under continuous monitoring, Proceedings – Annual Reliability and Maintainability Symposium, 2011.
• [5] Yang W., Tavner P. J., Court R.: An online technique for condition monitoring the induction generators used in wind and marine turbines, Mechanical Systems and Signal Processing, 38 (1) (2013) pp. 103-112.
• [6] Tchakoua P.,Wamkeue R., Tameghe T. A., Ekemb G.: A review of concepts and methods for wind turbines condition monitoring, 2013 World Congress on Computer and Information Technology (WCCIT), 2 (1) (2013) pp. 1-9.
• [7] Barszcz T., Strączkiewicz M.: Novel Intuitive Hierarchical Structure for Condition Monitoring System of Wind Turbines, Diagnostyka, 14 (3) (2013) pp. 53-60.
• [8] Hau E.: Wind Turbines: Fundamentals, Technologies, Application, Economics, Springer, 2006.
• [9] McMillan D., Ault G.: Condition monitoring benefit for onshore wind turbines: sensitivity to operational parameters, Renewable Power Generation, IET 2 (1) (2008) pp. 60-72.
• [10] Buscarello R. T.: Practical Solutions to Machinery and Maintenance Vibration Problems, Update International; Fourth revised edition, 2002.
• [11] Martin N., Mailhes C.: A non-stationary index resulting from time and frequency domains, in: Sixth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies. CM 2009 and MFPT 2009, Dublin, Ireland, 2009.
• [12] Mailhes C., Martin N., Sahli K., Lejeune G.: A spectral identity card, in: EUropean SIgnal Processing Conference, EUSIPCO 06, Florence, Italy, 2006.
• [13] Mailhes C., Martin N., Sahli K., Lejeune G.: Condition monitoring using automatic spectra analysis, in: Special session on "Condition Monitoring of Machinery", Third European Workshop on Structural Health Monitoring, Structural Health Monitoring 2006, Granada, Spain, 2006, pp. 1316-1323.
• [14] Gerber T., Martin N., Mailhes C.: Identification of harmonics and sidebands in a finite set of spectral components, in: Tenth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies. CM 2013 and MFPT 2013, Kraków, Poland, 2013.
• [15] Firla M., Li Z.-Y., Martin N., Barszcz T.: Automatic and Full-band Demodulation for Fault Detection - Validation on a Wind Turbine Test Rig, in: 4th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO'2014), France, Lyon, 2014.
• [16] Gelman L., Kripak D., Fedorov V., Udovenko L.: Condition Monitoring Diagnosis Methods of Helicopter Units, Mechanical Systems and Signal Processing, 14 (4) (2000) pp. 613-624.
• [17] Randall R. B., Antoni J.: Rolling element bearing diagnostics – A tutorial, Mechanical Systems and Signal Processing, 25 (2) (2011) pp. 485-520.
• [18] Randall R. B.: Vibration-based Condition Monitoring, John Wiley & Sons, Ltd, Chichester, UK, 2011.
• [19] Fyfe K., Munck E.: Analysis of computed order tracking, Mechanical Systems and Signal Processing, 11 (2) (1997) pp. 187-205.
• [20] Li Z.-Y., Gerber T., Firla M., Bellemain P., Martin N., Mailhes C.: AStrion strategy: from acquisition to diagnosis. Application to wind turbine monitoring, in: Twelve International Conference on Condition Monitoring and Machinery Failure Prevention Technologies. CM 2015, Oxford, United Kingdom, 2015.
• [21] Gerber T., Martin N., Mailhes C.: Timefrequency Tracking of Spectral Structures Estimated by a Data-driven Method, IEEE Transactions on Industrial Electronics, Special Section on: Condition Monitoring, Diagnosis, Prognosis, and Health Management for Wind Energy Conversion Systems, 62 (10) (2015) pp. 6616-6626.