The Use of the Acoustic Signal to Diagnose Machines Operated Under Variable Load

• Autorzy: Pawlik Paweł

Identyfikatory

DOI

10.24425/aoa.2020.133147

• Języki publikacji: EN

Abstrakty

EN

Acoustic signal is more and more frequently used to diagnose machines operated in industrial conditions where installation of sensors is hindered. Impact of background noise seems to be the major problem as part of analysis of such signal. In most cases of industrial environments, background level is high; thus, it prevents against concluding as per standard methods that have been used in diagnostic testing. This study specifies the problem related to diagnosing machines operated under variable loads. Synchronous methods are used for diagnosing these types of machines, those include synchronisation of diagnostic signal with revolutions of the diagnosed machine. For the purpose of this study an acoustic signal was used as the diagnostic signal. Application of the synchronous method (order analysis) enables eliminating an impact of background noise derived from other sources. This study specifies application of acoustic signal to diagnose planetary gear in laboratory testing rig in order to discover damages at early stage of degradation. This method was compared with the method basing on measurement of vibrations.

Słowa kluczowe

EN

acoustic diagnostics; vibroacoustics; order analysis

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2020
• Tom: Vol. 45, No. 2
• Strony: 263--270
• Opis fizyczny: Bibliogr. 26 poz., fot., rys., tab., wykr.

Twórcy

autor

Pawlik Paweł

• AGH University of Science and Technology, Faculty of Mechanical Engineering and Robotics, Department of Mechanics and Vibroacoustics, al. Mickiewicza 30, 30-059 Kraków, Poland

Bibliografia

• 1. Baydar N., Ball A. (2001), A comparative study of acoustic and vibration signals in detection of gear failures using Wigner-Ville distribution, Mechanical Systems and Signal Processing, 15 (6): 1091-1107, doi: 10.1006/MSSP.2000.1338.
• 2. Baydar N., Ball A. (2003), Detection of gear failures via vibration and acoustic signals using wavelet transform, Mechanical Systems and Signal Processing, 17 (4): 787-804, doi: 10.1006/MSSP.2001.1435.
• 3. Braun S. G., Seth B. B. (1979), On the extraction and filtering of signals acquired from rotating machines, Journal of Sound and Vibration, 65 (1): 37-50, doi: 10.1016/0022-460X(79)90526-1.
• 4. Burdzik R., Konieczny Ł., Warczek J., Cioch W. (2017), Adapted linear decimation procedures for TFR analysis of non-stationary vibration signals of vehicle suspensions, Mechanics Research Communications, 82: 29-35, doi: 10.1016/j.mechrescom.2016.11.002.
• 5. Cioch W., Knapik O., Leśkow J. (2013), Finding a frequency signature for a cyclostationary signal with applications to wheel bearing diagnostics, Mechanical Systems and Signal Processing, 38 (1): 55-64, doi: 10.1016/j.ymssp.2012.12.013.
• 6. Dąbrowski D. (2016), Condition monitoring of planetary gearbox by hardware implementation of artificial neural networks, Measurement: Journal of the International Measurement Confederation, 91: 295-308, doi: 10.1016/j.measurement.2016.05.056.
• 7. Dąbrowski Z., Dziurdź J., Klekot G. (2017), Influence of the mesh geometry evolution on gearbox dynamics during its maintenance, International Journal of Applied Mechanics and Engineering, 22 (4): 1097-1105, doi: 10.1515/ijame-2017-0071.
• 8. Jaramillo V. H., Ottewill J. R., Dudek R., Lepiarczyk D., Pawlik P. (2017), Condition monitoring of distributed systems using two-stage Bayesian inference data fusion, Mechanical Systems and Signal Processing, 87: 91-110, doi: 10.1016/j.ymssp.2016.10.004.
• 9. Łazarz B., Peruń G. (2012), Influence of construction factors on the vibrational activity of the gearing, Transport Problems, 7 (2): 95-102.
• 10. Łazarz B., Wojnar G., Czec P. (2011), Early fault detection of toothed gear in exploitation conditions, Eksploatacja i Niezawodnosc – Maintenance and Reliability, 1 (49): 68-77, retrieved from http://www.ein.org.pl/2011-01-09.
• 11. Lei Y., Lin J., Zuo M. J., He Z. (2014), Condition monitoring and fault diagnosis of planetary gearboxes: A review, Measurement, 48: 292-305, doi: 10.1016/j.measurement.2013.11.012.
• 12. Lenart Ł., Leśkow J., Synowiecki R. (2008), Subsampling in testing autocovariance for periodically correlated time series, Journal of Time Series Analysis, 29 (6): 995-1018, doi: 10.1111/j.1467-9892.2008.00591.x.
• 13. Li Y., Feng K., Liang X., Zuo M. J. (2019), A fault diagnosis method for planetary gearboxes under non-stationary working conditions using improved Vold-Kalman filter and multi-scale sample entropy, Journal of Sound and Vibration, 439: 271-286, doi: 10.1016/j.jsv.2018.09.054.
• 14. National Instruments Corporation (2005), LabVIEW. Order Analysis Toolkit User Manual, Austin, Texas.
• 15. Ottewill J. R., Orkisz M. (2013), Condition monitoring of gearboxes using synchronously averaged electric motor signals, Mechanical Systems and Signal Processing, 38 (2): 482-498, doi: 10.1016/j.ymssp.2013.01.008.
• 16. Pan M.-C., Chiu C.-C. (2006), Investigation on improved Gabor order tracking technique and its applications, Journal of Sound and Vibration, 295 (3-5): 810-826, doi: 10.1016/j.jsv.2006.01.046.
• 17. Parey A., Singh A. (2019), Gearbox fault diagnosis using acoustic signals, continuous wavelet transform and adaptive neuro-fuzzy inference system, Applied Acoustics, 147: 133-140, doi: 10.1016/J.APACOUST.2018.10.013.
• 18. Pawlik P. (2019), Single-number statistical parameters in the assessment of the technical condition of machines operating under variable load, Eksploatacja i Niezawodnosc – Maintenance and Reliability, 21 (1): 164-169, doi: 10.17531/ein.2019.1.19.
• 19. Pawlik P. et al. (2016), Vibroacoustic study of powertrains operated in changing conditions by means of order tracking analysis, Eksploatacja i Niezawodnosc – Maintenance and Reliability, 18 (4): 606-612, doi: 10.17531/ein.2016.4.16.
• 20. Popiołek K., Pawlik P. (2016), Diagnosing the technical condition of planetary gearbox using the artificial neural network based on analysis of non-stationary signals, Diagnostyka, 17 (2): 57-64.
• 21. Randall R. B. (1987), Frequency Analysis, Nærum: Bruel & Kjær.
• 22. Randall R. B., Antoni J. (2011), Rolling element bearing diagnostics – A tutorial, Mechanical Systems and Signal Processing, 25 (2): 485-520, doi: 10.1016/j.ymssp.2010.07.017.
• 23. Shao H., Jin W., Qian S. (2003), Order tracking by discrete gabor expansion, IEEE Transactions on Instrumentation and Measurement, 52 (3): 754-761, doi: 10.1109/TIM.2003.814670.
• 24. Stępień B. (2018), A Comparison of Classical and Bayesian Interval Estimation for Long-Term Indicators of Road Traffic Noise, Acta Acustica United with Acustica, 104 (6): 1118-1129, doi: 10.3813/AAA.919276.
• 25. Urbanek J., Barszcz T., Strączkiewicz M., Jablonski A. (2017), Normalization of vibration signals generated under highly varying speed and load with application to signal separation, Mechanical Systems and Signal Processing, 82: 13-31, doi: 10.1016/j.ymssp.2016.04.017.
• 26. Zhang M., Wang K., Wei D., Zuo M. J. (2018), Amplitudes of characteristic frequencies for fault diagnosis of planetary gearbox, Journal of Sound and Vibration, 432: 119-132, doi: 10.1016/j.jsv.2018.06.011.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).