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Albarbar, A.; Gu, Fengshou; Ball, Andrew (2010)
Publisher: Elsevier
Languages: English
Types: Article
Subjects: TJ, TL
Air-borne acoustic based condition monitoring is a promising technique because of its intrusive nature and the rich information contained within the acoustic signals including all sources. However, the back ground noise contamination, interferences and the number of Internal Combustion Engine ICE vibro-acoustic sources preclude the extraction of condition information using this technique. Therefore, lower energy events; such as fuel injection, are buried within higher energy events and/or corrupted by background noise.\ud \ud This work firstly investigates diesel engine air-borne acoustic signals characteristics and the benefits of joint time-frequency domain analysis. Secondly, the air-borne acoustic signals in the vicinity of injector head were recorded using three microphones around the fuel injector (120° apart from each other) and an Independent Component Analysis (ICA) based scheme was developed to decompose these acoustic signals. The fuel injection process characteristics were thus revealed in the time-frequency domain using Wigner-Ville distribution (WVD) technique. Consequently the energy levels around the injection process period between 11 and 5 degrees before the top dead center and of frequency band 9 to 15 kHz are calculated. The developed technique was validated by simulated signals and empirical measurements at different injection pressure levels from 250 to 210 bars in steps of 10 bars. The recovered energy levels in the tested conditions were found to be affected by the injector pressure settings.
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    • 1.A. Parlak, H. Yasar, C. Hasimoglu, A. Kolip (2005). The effects of injection timing on NOx emissions of a low heat rejection indirect diesel injection engine. Applied Thermal Engineering, Volume 25, 3042-3052.
    • 2.T. Priede (1967). Noise of diesel engine injection equipment. Journal of Sound and Vibration, 6(3), 443-459.
    • 3.Q. Hu, S. F. Wu, S. Stottler, R. Raghupathi (2001). Modeling of dynamic responses of an automotive fuel rail system. Part I: Injector. Journal of Sound and Vibration, 245 (5), 801-814.
    • 4.R. S. F. Wu, Q. Hu, S. Stottler, R. Raghupathi (2001). Modeling of dynamic responses of an automotive fuel rail system. Part II: entire system. Journal of Sound and Vibration, 245(5), 815-834.
    • 5.F. Gu, A. D. Ball (1996), Diesel injector dynamics modelling and estimation injection parameters from impact response. Part I: modelling and analysis of injector impacts. Proceedings of Institution of Mechanical Engineers. Part D: Journal of. Automobile Engineering 210(4) 293-302.
    • 6.F. Gu, A. D. Ball, K.K. Rao (1996), Diesel injector dynamics modelling and estimation injection parameters from impact response. Part II: prediction of injection parameters from monitored vibration, Proceedings of Institution of Mechanical Engineers. Part D: Journal of. Automobile Engineering 210(4) 303-312.
    • 7.J.D. Gill, R.L. Reuben, J.A. Steel (2000), A study of small HSDI diesel engine fuel injection equipment faults using acoustic emission, in: Proceedings of the EWGAE, 24th European Conference on Acoustic Emission Testing, Paris, France, 281-286.
    • 8.P. Nivesrangsan, J.A. Steel, R.L. Reuben (2007). Acoustic emission mapping of diesel engines for spatially located time series-Part II: Spatial reconstitution. Mechanical Systems and Signal Processing, Volume 21(2), 1084-1102.
    • 9.A. Albarbar (2006), The acoustic condition monitoring of diesel engines, Ph.D. thesis, University of Manchester.
    • 10. A. Albarbar, R Gennish, F. Gu, A. D. Ball (2004), Lubrication oil condition monitoring using vibration and air-borne acoustic measurements. The 7th Biennial ASME Conference on Engineering Systems Design and Analysis, ESDA04, Manchester, July 2004, 58360.
    • 11. A. Albarbar, F. Gu, A. Ball, A. Starr (2007), Internal combustion engine lubricating oil condition monitoring based on vibro-acoustic measurements. Journal of Non Destructive Testing Institution. 49:p.715-719.
    • 12. A. Albarbar, F. Gu, A. Ball, A. Starr (2008), On acoustic measurement based internal combustion engines condition monitoring. Journal of Non-destructive Testing Institution. Insight, 50:p. 30-34.
    • 13. A. Albarbar, S. Alhashmi, R. Gennish, F. Gu, A. D. Ball (2004), Adaptive noise cancelling for enhancing diesel engine air-borne acoustic signal to noise ratio. COMADEM04, Cambridge, pp. 334-343.
    • 14. A. Albarbar, F. Gu, A. D. Ball, A. Starr (2010), Acoustic monitoring of engine fuel injection based on adaptive filtering tecgniques. Applied Acoustics, in press, 10.1016/j.apacoust.2010.07.001.
    • 15. G. Gelle, M. Colas, C. Serviere (2003). Blind Source Separation: A new pre-processing tool for rotating machines monitoring, IEEE Transactions on Instrumentation and Measurement 52(3), 790- 795.
    • 16. M. Roam, M. Erling, L. Sibul (2005). A new non-linear adaptive blind source separation approach to gear tooth failure detection and analysis, Mechanical Systems and Signal Processing 16 (5), 719- 740.
    • 17. M. Knaak, M. Kunter, D. Filberi (2002). Blind source separation for acoustical machine diagnosis, the 14th International Conference on Digital Signal Processing, Aegean Island of Santorini (Thera), Greece, 159-162.
    • 18. A. Ypma, D. Tax, R. Duin. Robust machine fault detection with independent component analysis and support vector data description, Proceedings of IEEE International Workshop on Neural Networks for Signal Processing, Madison, WI (USA), 23-25 1999, 67-76.
    • 19. X. Tian, J. Lin, K. Fyfe, M. Zuo (2003), Gearbox fault diagnosis using independent component analysis in the frequency domain and wavelet filtering, Proceedings of 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '03) 2(6-10). 245-248.
    • 20. W. Li. F. Gu, A. Ball, A., Leung, A., Phipps (2001). A Study of the Noise from Diesel Engines Using the Independent Component Analysis. Mechanical Systems and Signal Processing 15(6), 1165-1184.
    • 21. P. Pajunen, A. Hyvärinen, J. Karhunen (1996), Non-linear blind source separation by selforganizing maps, Proceedings of the International Conference on Neural Information Processing, Hong Kong, China, pp. 1207-1210.
    • 22. A. Hyvärinen (1999), Fast and robust fixed-point algorithms for independent component analysis, IEEE Transactions on Neural Networks 10(3), pp. 626-634.
    • 23. A. Hyvärinen, E. Oja (1997), A fast fixed-point algorithm for independent component analysis, Neural Computation 9(7), pp. 1483-1492.
    • 24. E. Bingham (2003). Advances in independent component analysis with applications to data mining. PhD thesis, Helsinki University of Technology, Finland.
    • 25. A. Albarbar, M. Regea, M. Elhaj, F. Gu, A. D. Ball (2004), Independent component analysis for enhancing diesel engine air-borne acoustics signal to noise ratio, in: Proceeding of 9th Mechatronics Forum International Conference, Mechatronics04, Ankara, pp 197-207.
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