Fault detection and diagnosis of a 12-cylinder trainset diesel engine based on vibration signature analysis and neural network

Abstract

This paper presents a condition monitoring and combustion fault detection technique for a 12-cylinder 588 kW trainset diesel engine based on vibration signature analysis using fast Fourier transform, discrete wavelet transform, and artificial neural network. Most of the conventional fault diagnosis techniques in diesel engines are mainly based on analyzing the difference of vibration signals amplitude in the time domain or frequency spectrum. Unfortunately, for complex engines, the time- or frequency-domain approaches do not provide appropriate features solely. In the present study, vibration signals are captured from both intake manifold and cylinder heads of the engine and were analyzed in time-, frequency-, and time–frequency domains. In addition, experimental data of a 12-cylinder 588 kW diesel engine (of a trainset) are captured and the proposed method is verified via these data. Results show that power spectra of vibration signals in the low-frequency range reliably distinguish between normal and faulty conditions. However, they cannot identify the fault location. Hence, a feature extraction method based on discrete wavelet transform and energy spectrum is proposed. The extracted features from discrete wavelet transform are used as inputs in a neural network for classification purposes according to the location of sensors and faults. The experimental results verified that vibration signals acquired from intake manifold have more potential in fault detection. In addition, the capacity of discrete wavelet transform and artificial neural network in detection and diagnosis of faulty cylinders subjected to the abnormal fuel injection was revealed in a complex diesel engine. Beside condition monitoring of the engine, a two-step fault detection method is proposed, which is more reliable than other one-step methods for complex engines. The average condition monitoring performance is from 93.89% up to 99.17%, based on fault location and sensor placement, and the minimum classification performance is 98.34%.

Keywords

Fault detection condition monitoring vibration signal neural network discrete wavelet transform trainset diesel engine

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References

Jones

. A review of condition monitoring and fault diagnosis for diesel engines. Tribotest 2000; 6: 267–2691.

Traver ML, Atkinson RJ and Atkinson CM. Neural network-based diesel engine emissions prediction using in-cylinder combustion pressure. SAE Technical Paper, 1999.

Yan

Yuan

et al.

Intelligent fault diagnosis method for marine diesel engines using instantaneous angular speed. J Mech Sci Technol 2012; 26: 2413–2423.

Assanis

Friedmann

. A thin-film thermocouple for transient heat transfer measurements in ceramic-coated combustion chambers. Int Commun Heat Mass Transfer 1993; 20: 459–468.

Albarbar

Ball

. Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis. Measurement 2010; 43: 1376–1386.

Jiang

Cao

Wei

. NOx sensor ammonia cross-sensitivity estimation with adaptive unscented Kalman filter for Diesel-engine selective catalytic reduction systems. Fuel 2016; 165: 185–192.

Wang

Zhang

Cui

et al.

Fault diagnosis for manifold absolute pressure sensor (MAP) of diesel engine based on Elman neural network observer. Chin J Mech Eng 2016; 29: 386–395.

Ning

Sun

Gong

et al.

Extraction of fault component from abnormal sound in diesel engines using acoustic signals. Mech Syst Signal Process 2016; 75: 544–555.

Moosavian

Najafi

Ghobadian

et al.

Piston scuffing fault and its identification in an IC engine by vibration analysis. Appl Acoust 2016; 102: 40–48.

10.

Chen

Randall

Peeters

. Advanced diagnostic system for piston slap faults in IC engines, based on the non-stationary characteristics of the vibration signals. Mech Syst Signal Process 2016; 75: 434–454.

11.

Taghizadeh-Alisaraei

Ghobadian

Tavakoli-Hashjin

et al.

Characterization of engine’s combustion-vibration using diesel and biodiesel fuel blends by time-frequency methods: A case study. Renew Energy 2016; 95: 422–432.

12.

Heneghan

Khanna

Flock

et al.

Investigating the nonlinear dynamics of cellular motion in the inner ear using the short-time Fourier and continuous wavelet transforms. IEEE Trans Signal Process 1994; 42: 3335–3352.

13.

Oehlmann

Brie

Tomczak

et al.

A method for analysing gearbox faults using time–frequency representations. Mech Syst Signal Process 1997; 11: 529–545.

14.

Staszewski

Worden

Tomlinson

. Time–frequency analysis in gearbox fault detection using the Wigner–Ville distribution and pattern recognition. Mech Syst Signal Process 1997; 11: 673–692.

15.

Baydar

Ball

. Detection of gear failures via vibration and acoustic signals using wavelet transform. Mech Syst Signal Process 2003; 17: 787–804.

16.

Butler-Purry

Bagriyanik

. Characterization of transients in transformers using discrete wavelet transforms. IEEE Trans Power Syst 2003; 18: 648–656.

17.

Geng

Chen

. Investigation into piston-slap-induced vibration for engine condition simulation and monitoring. J Sound Vib 2005; 282: 735–751.

18.

Teraguchi S, Suzuki W, Takiguchi M, et al. Effects of lubricating oil supply on reductions of piston slap vibration and piston friction. SAE Technical Paper, 2001.

19.

Gideon G, Gal de B BR and Eran S. Assessment of the quality of combustion in compression ignition engines through vibration signature analysis. Technical paper for students and young engineers, Fisita World Automotive Congress, Barcelona-Beer sheva, 2004, p. 2004.

20.

Carlucci

Chiara

Laforgia

. Analysis of the relation between injection parameter variation and block vibration of an internal combustion diesel engine. J Sound Vib 2006; 295: 141–164.

21.

Keskin

. The influence of ethanol–gasoline blends on spark ignition engine vibration characteristics and noise emissions. Energy Sources A 2010; 32: 1851–1860.

22.

Shirazi F and Mahjoob M. Application of discrete wavelet transform (DWT) in combustion failure detection of IC engines. In: 2007 ISPA 2007 5th international symposium on image and signal processing and analysis, 2007, pp.482–486. New York: IEEE.

23.

Heywood

. Internal combustion engine fundamentals, New York: McGraw-Hill, 1988.

24.

Liu

Ball

. Detection of engine valve faults by vibration signals measured on the cylinder head. Proc IMechE, Part D: J Automobile Engineering 2006; 220: 379–386.

25.

Jiang

Gennish

et al.

Monitoring of diesel engine combustions based on the acoustic source characterisation of the exhaust system. Mech Syst Signal Process 2008; 22: 1465–1480.

26.

Wang

Zhang

Zhong

. Fault diagnosis for diesel valve trains based on time–frequency images. Mech Syst Signal Process 2008; 22: 1981–1993.

27.

Flett

Bone

. Fault detection and diagnosis of diesel engine valve trains. Mech Syst Signal Process 2016; 72: 316–327.

28.

Cerna M and Harvey AF. The fundamentals of FFT-based signal analysis and measurement. National Instruments, Junho, 2000.

29.

Peng

Yam

. Wavelet analysis and envelope detection for rolling element bearing fault diagnosis—their effectiveness and flexibilities. J Vib Acoust 2001; 123: 303–310.

30.

Burrus CS, Gopinath RA and Guo H. Introduction to wavelets and wavelet transforms: A primer. Upper Saddle River: Prentice-Hall, 1997.

31.

J-D

Hsu

C-C

G-Z

. Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference. Expert Syst Appl 2009; 36: 6244–6255.

32.

Gaing

Z-L

. Wavelet-based neural network for power disturbance recognition and classification. IEEE Trans Power Deliv 2004; 19: 1560–1568.

33.

Saravanan

Ramachandran

. Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN). Expert Syst Appl 2010; 37: 4168–4181.

34.

Ablameyko S. Neural networks for instrumentation, measurement and related industrial applications. Amsterdam, Netherlands: IOS Press, 2003.

35.

Browne

. Neural network analysis, architectures and applications, Boca Raton, FL: CRC Press, 1997.

36.

Lei

. A combination of WKNN to fault diagnosis of rolling element bearings. J Vib Acoust 2009; 131: 064502–064502.