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Mishra, R; Gu, F; Fazenda, BM; Stubbs, C; Ball, A
Languages: English
Types: Unknown
Subjects: QC221246, other

Classified by OpenAIRE into

mesheuropmc: otorhinolaryngologic diseases
Engine intake system leaks are a primary reason behind unsatisfactory operational and emission characteristics of turbocharged engines. In faulty conditions, the intake mass flow sensed by the mass flow sensor does not reach the combustion chamber, resulting in a less than required air fuel mass ratio. This may result in increased emissions along with unsatisfactory performance of exhaust gas\ud recirculation system. In the present work, an acoustic sniffer system has been developed which identifies the presence and location of leaks in the intake system. The acoustic sniffer system uses a directional acoustic probe which records acoustic signatures of the leaks and positively identifies their presence. The sound pressure level data obtained from the acoustic probe is converted into frequency domain by FFT. Frequencies corresponding to the leaks are then identified in different octave bands.\ud Experimental work on a four stroke 4.3 litre diesel engine demonstrates that the sniffer could be used to identify and quantify the leaks in the engine system. The system performs satisfactorily on a variety of leaks created in the intake system. It has also been found that sound pressure levels and frequency bands corresponding to different leaks could be used to estimate the amount of gas leaked from the\ud system. This acoustics based system provides a simple and yet reliable method for leak diagnosis in engine intake system.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1) Antory, D., Application of a data-driven monitoring technique to diagnose air leaks in an automotive diesel engine: A case study', Mechanical Systems and Signal Processing Volume 21, Issue 2, February 2007, Pages 795-808.
    • 2) Matthew A. Franchek, Patrick J. Buehler, Imad Makki, ' Intake Air Path Diagnostics for Internal Combustion Engines', Journal of Dynamic Systems, Measurement, and Control, Volume 129, Number 1 (January 2007).
    • 3) Kimmich, F. Schwarte, A. Isermann, R.,' Fault detection for modern Diesel engines using signal- and process modelbased methods', Control Engineering Practice, Volume 13, Issue 2, February 2005, Pages 189-203.
    • 4)Sangha, Mahavir SGomm, J. Barry Yu, Dingli, Neural network fault classification of transient data in an automotive engine air path, International Journal of Modelling, Identification and Control 20080101 3(2): 8.
    • 5) Wu, J.D. ,Chiang, P.H.,Chang, Y.W.,Shiao, Y.j., An expert system for fault diagnosis in internal combustion engines using probability neural network, Expert Systems With Applications ,34(4),p.2704-2713, May 2008.
    • 6) Wu, J.D., Chen, J.C., Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines, NDT and E International ,39(4),p.304-311, Jun 2006.
    • 7) Olson, H. F., Gradient Microphones, Journal of the Acoustical Society of America, Vol. 17, No 3, 1946
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