Internal combustion engine sound-based fault detection and diagnosis using adaptive line enhancers

Abstract

In an internal combustion engine, the impulsive sounds are very often radiated owing to the faults of the engine. Thus it is important for a noise, vibration, and harshness engineer to detect and analyse impulsive sound signals for both fault diagnoses. However, it is often difficult to detect and identify impulsive signals because of interfering signals such as those due to engine firing, harmonics of crankshaft speed, and broadband noise components. These interferences hinder the early detection of faults and improvement of sound quality. In order to overcome this difficulty, a two-stage adaptive line enhancer which is capable of enhancing impulsive signals embedded in background noise is developed. This method is used to pre-process signals prior to time—frequency analysis via a bilinear methods such as the Wigner—Ville distribution and the Choi—Williams distribution.

Keywords

adaptive filter impact sound time frequency fault engine

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References

Braun

Mechanical signature analysis - theory and applications, 1986 (Academic Press, New York).

Braun

S. G.

Seth

B. B.

On the extraction and filtering of signals acquired from rotating machines. J. Sound Vibr., 1979, 65(1), 37–50.

Lee

S. K.

White

P. R.

Fault diagnosis of rotating machinery using a two-stage ALE (adaptive line enhancer). In Proceedings of the IEE Colloquium on Modelling and signal processing fault diagnosis, 1996, pp. IEEE, London.

Priede

Origin of automotive engine and vehicle noise. Memorandum 263, ISVR, University of Southampton, 1968.

Lee

S. K.

Weight reduction and noise refinement of 1.5 liter, new developments in engine design and engine component technology (SP-1017). SAE Paper 940995, 1994.

Widrow

Glorer

J. R.

McCool

J. M.

Williams

C. S.

Heavn

R. H.

Zeidler

J. R.

Dong

Goodlin

R. C.

Adaptive noise cancelling: Principles and application. Proc. IEEE, 1975, 63, 1692–1716.

Haykin

Adaptive filter theory, 4th edition, 2001 (Prentice Hall, Upper Saddle River, New Jersey).

Sayed

A. H.

Fundamentals of adaptive filtering, 2003 (John Wiley-IEEE, New York).

Cohen

Time-frequency analysis, 1995 (Prentice-Hall, Englewood Cliffs, New Jersey).

10.

Wignera

On the quantum correlation for thermodynamic equilibrium. Phys. Rev., 1932, 40, 747–759.

11.

Hammond

J. K.

White

P. R.

The analysis of non-stationary signals using the time-frequency methods. J. Sound Vibr., 1996, 190(3), 19–447.

12.

Flandrin

Representation of multicomponent signal. In Proceedings of the IEEE International Conference on Accoustics, speech and signal processing (ICASSP, 84), 1984, pp. IEEE, New York.

13.

Choi

H. I.

Williams

Improved time-frequency representation of multiple component signal using exponential Kernel. IEEE Trans. Acoust. Speech Signal Processing, 1989, 37, 862–871.

14.

Zhao

Atlas

Marks

The use of coneshape kernels for generalised time-frequency representations of non-stationary signals. IEEE Trans. Acoust. Speech Signal Processing, 1990, 38(7), 804–1091.

15.

Jones

D. L.

Baraniuk

R. G.

An adaptive optimal kernel time-frequency representation. IEEE Trans. Acoust. Speech Signal Processing, 1995, 43(10), 256–236.

16.

Lee

S. K.

Identification of a vibration transmission path in vehicle by measuring vibration power flow. Proc. Instn Mech. Engrs Part D: J. Automobile Engineering, 2004, 218(D2), 167–175.

17.

Lee

S. K.

Objective evaluation of the rumbling sound in passenger cars based on an artificial neural network. Proc. IMechE Part D: J. Automobile Engineering, 2005, 219(4), 457–469.