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Combustion Event Detection in a Single Cylinder Diesel Engine by Analysis of Sound Signal Recorded by Android Mobile

  • Sankar Kumar RoyEmail author
Conference paper
  • 450 Downloads
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

Combustion event detection is an important issue in internal combustion engine. The combustion is mostly detected by measurement of pressure by a pressure sensor which is mounted in cylinder head. However, the cost of pressure sensor is very high. Therefore, an effort has been made to detect the combustion event in a diesel engine by analysis of sound signal recorded by android mobile. The sound signal carries various frequencies. Therefore, an algorithm based on wavelet packet transform (WPT) has been developed to detect the combustion event in a single cylinder diesel engine.

Keywords

Diesel engine Sound signal Android mobile phone 

References

  1. 1.
    Heywood JB (1988) Internal combustion engine fundamentals. McGraw-Hill, New YorkGoogle Scholar
  2. 2.
    Mobley C (1999) Non-intrusive in-cylinder pressure measurement of internal combustion engines. SAE Paper, 1999-01-0544Google Scholar
  3. 3.
    Lyon RH, DeJong RG (1984) Design of a high-level diagnostic system. J Vib Acoust 106:17–21CrossRefGoogle Scholar
  4. 4.
    Gao Y, Randall RB (1999) Reconstruction of diesel engine cylinder pressure using a time domain smoothing technique. Mech Syst Signal Process 13:709–722CrossRefGoogle Scholar
  5. 5.
    Antoni J, Daniere J, Guillet F (2002) Effective vibration analysis of IC engines using cyclostationarity; A methodology for condition monitoring—part 1. J Sound Vib 257:815–837CrossRefGoogle Scholar
  6. 6.
    Antoni J, Daniere J, Guillet F (2002) Effective vibration analysis of IC engines using cyclostationarity; A methodology for condition monitoring-Part 2. J Sound Vib 257:839–856CrossRefGoogle Scholar
  7. 7.
    Connolly FT, Yagle AE (1992) Modeling and identification of the combustion pressure process in internal combustion engines using engine speed fluctuations. In: American society of mechanical engineers, dynamic systems and control division, vol 44. Anaheim, CA, pp 191–206Google Scholar
  8. 8.
    Brand D, Onder C, Guzzella L (2005) Estimation of the instantaneous in-cylinder pressure for control purposes using crankshaft angular velocity. In Proceedings: SAE world congress paper 2005-01-0228, (2005)Google Scholar
  9. 9.
    Moro D, Cavina N, Ponti F (2002) In-cylinder pressure reconstruction based on instantaneous engine speed signal. J Eng Gas Turbines Power 124:220–225CrossRefGoogle Scholar
  10. 10.
    Yang J, Pu L, Wang Z, Zhou Y, Yan X (2001) Fault detection in a diesel engine by analysing the instantaneous angular speed. Mech Syst Signal Process 15:549–564CrossRefGoogle Scholar
  11. 11.
    Rizzoni G (1989) Diagnosis-of individual cylinder misfires by signature analysis of crankshaft speed fluctuations. SAE Paper No. 890884Google Scholar
  12. 12.
    Ponti F (2008) Instantaneous engine speed time-frequency analysis for onboard misfire detection and cylinder isolation in a V12 high-performance engine. J Eng Gas Turbines Power 130:1–9Google Scholar
  13. 13.
    Charles P, Sinha JK, Gu F, Lidstone L, Ball AD (2009) Detecting the crankshaft torsional vibration of diesel engines for combustion related diagnosis. J Sound Vib 321:1171–1185CrossRefGoogle Scholar
  14. 14.
    Charles P, Sinha JK, Gu F, Ball AD (2010) Application of novel polar representation method for monitoring minor engine condition variations. Mech Syst Signal Process 24:841–843CrossRefGoogle Scholar
  15. 15.
    Roy SK, Mohanty AR (2017) Use of rotary optical encoder for firing detection in a spark ignition engine. Measurement 98:60–67CrossRefGoogle Scholar
  16. 16.
    Kamin´ski T, Wendeker M, Urbanowicz K, Litak G (2004) Combustion process in a spark ignition engine: Dynam. Noise level estimation. Chaos, 14, 401–406 (2004)Google Scholar
  17. 17.
    Jeong-Guon I, Kim HJ, Lee SH, Shinoda K (2009) Prediction of intake noise of an automotive engine in run-up condition. Appl Acoust 70(2):347–355CrossRefGoogle Scholar
  18. 18.
    Delvecchio S, Bonfiglio P, Pompoli F (2018) Vibro-acoustic condition monitoring of internal combustion engines: a critical review of existing techniques. Mech Syst Signal Process 99:661–683CrossRefGoogle Scholar
  19. 19.
    Irawan YS, Suyono H (2014) Bearing damage detection based on sound signal. Appl Mech Mater 548–549:698–702Google Scholar
  20. 20.
    Orman, M., Rzeszucinski, P., Tkaczyk, A., Krishnamoorthi, K., Pinto, C., Sulowicz, M.: Bearing fault detection with the use of acoustic signals recorded by a hand held mobile phone. In: second international conference on condition assessment techniques in electrical systems, IEEE CATCON 2015, pp. 252–256, Bangaluru (2015)Google Scholar
  21. 21.
    Siegel J, Kumar S, Ehrenberg I, Sarma S (2016) Engine misfire detection with pervasive mobile audio. In: Berendt B, Bringmann B, Fromont E, Garringa G, Miettinen P, Tatti N, Tresp V (eds) Machine learning and knowledge discovery in databases. ECML PKDD 2016. Springer, Riva Del Garda, Italy, pp 226–241CrossRefGoogle Scholar
  22. 22.
    Mohanty, A.R.: Machinery Condition Monitoring: Principles and Practices, 1st edn, CRC Press (2014)Google Scholar
  23. 23.
    Coifman RR, Meyer Y, Quake S, Wickerhauser MV (1994) Signal processing and compression with wavelet packets. In: Byrnes JS, Byrnes JL, Hargreaves KA, Berry K (Eds.), Wavelets and their applications, pp 363–379zbMATHGoogle Scholar
  24. 24.
    Fan X, Zuo MJ (2006) Gearbox fault detection using Hilbert and wavelet packet transform. Mech Syst Signal Process 20(4):966–982CrossRefGoogle Scholar
  25. 25.
    Vong CM, Wong PK (2011) Engine ignition signal diagnosis with wavelet packet transform and multi-class least squares support mector machines. Expert Syst Appl 38(7):8563–8570CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Mechanical Engineering DepartmentNational Institute Technology PatnaPatnaIndia

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