Application of FRF with SISO and MISO model for accelerometer-based in-cylinder pressure reconstruction on a 9-L diesel engine

Abstract

Engine control with feedback from engine combustion process diagnostics can help improve fuel efficiency and assist in meeting stricter emission regulations. The standard is to use in-cylinder pressure measurements with analysis including rate of heat release. The measurement is usually obtained with intrusive sensors that require a special mounting process and engine structure modification. The potential of the low-cost non-intrusive accelerometer as an alternative means to reconstruct the in-cylinder pressure has been demonstrated by previous investigations. In this work, start of injection (SOI) sweep test conditions at varied speed spanning both low load and high load were conducted on an inline 6-cylinder, 9 L diesel engine. The relationship between the in-cylinder pressure and the accelerometer signal was quantified with frequency response function (FRF). The robustness of the obtained FRF was evaluated by applying the single-test-based FRF to reconstruct the in-cylinder pressures for other test conditions. Two models, single-input single-output (SISO) and multiple-input single-output (MISO), were investigated and compared where the accelerometer signal was taken as the input and in-cylinder pressure as the output. The optimal channel used to acquire the input signal in the SISO model was selected on the basis of coherence analysis. Results show that the MISO model assisted by principal component analysis (PCA) and offset-compensation processes can result in better in-cylinder pressure estimation than the SISO model for conditions with 2200 rpm engine speed. With the purpose of minimizing the cost for accelerometer employment, the minimum number of inputs used to reconstruct the in-cylinder pressure in the MISO model was pursued. Thresholds were set based on three estimated in-cylinder pressure parameters to select the qualified input channels and two input channels were finally determined. Results showed that the two-input single-output FRF model coupled with the PCA and offset-compensation processes improves the FRF’s robustness for the in-cylinder pressure estimation in comparison to the SISO FRF model based on all the tests conducted in this paper.

Keywords

Frequency response function optimal accelerometer location selection in-cylinder pressure reconstruction SISO FRF model MISO FRF model

Get full access to this article

View all access options for this article.

References

Citron SJ, O’Higgins JE and Chen LY. Cylinder by cylinder engine pressure and pressure torque waveform determination utilizing speed fluctuations. SAE Technical Paper 890486, 1989. DOI:10.4271/890486.

Rizzoni G. Diagnosis-of individual cylinder misfires by signature analysis of crankshaft speed fluctuations. SAE Technical Paper 890884, 1989. DOI: 10.4271/890884.

Connolly

Yagle

. Modeling and identification of the combustion pressure process in internal combustion engines using engine speed fluctuations 1992; vol. 44, Anaheim, CA: American Society of Mechanical Engineers, Dynamic Systems and Control Division, pp. 191–206.

Naber JD, Blough JR, Frankowski D, et al. Analysis of combustion knock metrics in spark-ignition engines, SAE Paper 2006-01-0400.

Morello

Blough

Naber

. Signal processing parameters for estimation of the diesel engine combustion signature. SAE Int J Passeng Cars Mech Syst 2011; 4: 1201–1215.

Tohyama

Lyon

Koike

. Pulse waveform recovery in reverberant condition. Acoust Soc Am 1992; 91: 2805–2812.

Gao

Randall

. Reconstruction of diesel engine cylinder pressure using a time domain smoothing technique. Mech Syst Signal Process 1999; 13(5): 709–722.

Kim JT. Source and path recovery from vibration response monitoring. PhD Thesis, Massachusetts Institute of Technology, USA, 1987.

Johnsson

. Cylinder pressure reconstruction based on complex radial basis function networks from vibration and speed signals. Mech Syst Signal Process 2006; 20: 1923–1940.

10.

Jia L, Naber J, Blough J, et al. “Accelerometer-Based Combustion Metrics Reconstruction with Radial Basis Function Neural Network for a 9 L Diesel Engine.” Journal of Engineering for Gas Turbines and Power 136.3 (2014): 031507.

11.

Heywood

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

12.

Huang H, Su W and Pei Y. Experimental and numerical study of diesel HCCI combustion by multi-pulse injection. SAE technical paper 2008-01-0059.

13.

Beatrice

Belardini

Bertoli

. Diesel combustion control in common rail engines by new injection strategies. Int J Engine Res 2002; 3(1): 23–36.

14.

Chiavola O, Chiatti G and Recco E. Accelerometer measurements to optimize the injection strategy. SAE international, 2012-01-1341.

15.

Fassios

. MIMO LMS-ARMAX indentification of vibrating structures-part 1: the method. Mech Syst Signal Process 2001; 15: 723–735.

16.

Klerk

Ossipov

. Operational transfer path analysis: theory, guidelines and tire noise application. Mech Syst Signal Process 2010; 24: 1950–1962.

17.

Abdi H and Williams L. Princinpal component analysis. WIREs Computat Stat 2010; 2: 3--7.

18.

O’Toole

Abdi

. Deffenbacher KA and Valentin D. A low dimensional representation of faces in the higher dimensions of the space. J Opt Soc Am Ser A 1993; 10: 405–411.