Real time identification of the internal combustion engine combustion parameters based on the vibration velocity signal

Abstract Accurate combustion parameters are the foundations of effective closed-loop control of engine combustion process. Some combustion parameters, including the start of combustion, the location of peak pressure, the maximum pressure rise rate and its location, can be identified from the engine block vibration signals. These signals often include non-combustion related contributions, which limit the prompt acquisition of the combustion parameters computationally. The main component in these non-combustion related contributions is considered to be caused by the reciprocating inertia force excitation (RIFE) of engine crank train. A mathematical model is established to describe the response of the RIFE. The parameters of the model are recognized with a pattern recognition algorithm, and the response of the RIFE is predicted and then the related contributions are removed from the measured vibration velocity signals. The combustion parameters are extracted from the feature points of the renovated vibration velocity signals. There are angle deviations between the feature points in the vibration velocity signals and those in the cylinder pressure signals. For the start of combustion, a system bias is adopted to correct the deviation and the error bound of the predicted parameters is within 1.1°. To predict the location of the maximum pressure rise rate and the location of the peak pressure, algorithms based on the proportion of high frequency components in the vibration velocity signals are introduced. Tests results show that the two parameters are able to be predicted within 0.7° and 0.8° error bound respectively. The increase from the knee point preceding the peak value point to the peak value in the vibration velocity signals is used to predict the value of the maximum pressure rise rate. Finally, a monitoring frame work is inferred to realize the combustion parameters prediction. Satisfactory prediction for combustion parameters in successive cycles is achieved, which validates the proposed methods.

[1]  Kyoungdoug Min,et al.  Study on the Correlation between the Heat Release Rate and Vibrations from a Diesel Engine Block , 2015 .

[2]  Kang Ding,et al.  A new method for measuring engine rotational speed based on the vibration and discrete spectrum correction technique , 2013 .

[3]  Jien-Chen Chen,et al.  Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines , 2006 .

[4]  V. Sugumaran,et al.  Misfire detection in an IC engine using vibration signal and decision tree algorithms , 2014 .

[5]  Jyoti K. Sinha,et al.  Detecting the crankshaft torsional vibration of diesel engines for combustion related diagnosis , 2009 .

[6]  Jian-Da Wu,et al.  Fault diagnosis of internal combustion engines using visual dot patterns of acoustic and vibration signals , 2005 .

[7]  Antonio Paolo Carlucci,et al.  Analysis of the relation between injection parameter variation and block vibration of an internal combustion diesel engine , 2006 .

[8]  Robert B. Randall,et al.  Blind separation of internal combustion engine vibration signals by a deflation method , 2008 .

[9]  Carlos Guardiola,et al.  A methodology for combustion detection in diesel engines through in-cylinder pressure derivative signal , 2010 .

[10]  Roger Johnsson,et al.  Cylinder pressure reconstruction based on complex radial basis function networks from vibration and speed signals , 2006 .

[11]  Gianni Bidini,et al.  Diagnosis of internal combustion engine through vibration and acoustic pressure non-intrusive measurements , 2009 .

[12]  John Alexander Steel,et al.  Indirect measurement of cylinder pressure from diesel engines using acoustic emission , 2005 .

[13]  M. Muñoz,et al.  Diagnostic method based on the analysis of the vibration and acoustic emission energy for emergency diesel generators in nuclear plants , 2013 .

[14]  Jeffrey Naber,et al.  Signal Processing Parameters for Estimation of the Diesel Engine Combustion Signature , 2011 .

[15]  Mir Mohammad Ettefagh,et al.  Knock detection in spark ignition engines by vibration analysis of cylinder block : A parametric modeling approach , 2008 .

[16]  Ashkan Moosavian,et al.  Piston scuffing fault and its identification in an IC engine by vibration analysis , 2016 .

[17]  Ornella Chiavola,et al.  Diesel Engine Combustion Monitoring through Block Vibration Signal Analysis , 2009 .

[18]  Etienne Parizet,et al.  Diesel engine combustion and mechanical noise separation using an improved spectrofilter , 2009 .

[19]  Tang Juan,et al.  Combustion timing determination based on vibration velocity in HCCI engines , 2012 .

[20]  Jian-Da Wu,et al.  An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network , 2009, Expert Syst. Appl..

[21]  F. Taglialatela,et al.  Determination of combustion parameters using engine crankshaft speed , 2013 .

[22]  Paul King,et al.  Time-Frequency Analysis of Single-Point Engine-Block Vibration Measurements for Multiple Excitation-Event Identification , 2009 .