Diagnosis of sensor faults in a combustion engine control system with the artificial neural network
 
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University of Technology and Humanities in Radom
CORRESPONDING AUTHOR
Iwona Monika Komorska   

University of Technology and Humanities in Radom
Submission date: 2019-05-15
Final revision date: 2019-07-02
Acceptance date: 2019-07-02
Online publication date: 2019-07-04
Publication date: 2019-07-04
 
Diagnostyka 2019;20(4):19–25
 
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ABSTRACT
The work presents the investigations carried out on a spark-ignition internal combustion engine with gasoline direct injection. The tests were carried out under conditions of simulated damage to the air temperature sensor, engine coolant temperature sensor, fuel pressure sensor, air pressure sensor, intake manifold leakage, and air flow disturbances. The on-board diagnostic system did not detect any damage because the sensor indications were within acceptable limits. The engine control system in each case changed its settings according to the adaptive algorithm. Signal values in cycles from all available sensors in the engine control system and data available in the on-board diagnostic system of the car were recorded. A large amount of measurement data was obtained. They were used to create a statistical function that classifies sensor faults using an artificial neural network. A set of training data has been prepared accordingly. During learning the neural network, a hit rate of over 99% was achieved.
 
REFERENCES (24)
1.
Jurgen RK. Automotive Electronics Handbook. McGraw-Hill. New York 1995.
 
2.
Turner J. Automotive sensors. Momentum Press. New York 2009.
 
3.
Reif K. Gasoline Engine Management. Systems and Components. Springer-Vieweg 2015. https://doi.org/10.1007/978-3-....
 
4.
Merkisz J, Pielecha J, Radzimirski S. New trends in emission control in the European Union. Springer. 2014.
 
5.
Karagiorgis S, Glover K, Collings N. Control Challenges in Automotive Engine Management. European Journal of Control 2007. 13:92–104. https://doi.org/10.3166/EJC.13....
 
6.
Tang H, Weng L, Dong ZY, Yan R. Adaptive and Learning Control for SI Engine Model With Uncertainties. Mechatronics. IEEE/ASME Trans. 2009.14(1):93-104. https://doi.org/10.1109/TMECH.....
 
7.
Alt B, Svaricek F. Robust control design for automotive applications: A variable structure control approach. In book: Challenges and paradigms in applied robust control. IntechOpen 2011. https://doi.org/10.5772/16724.
 
8.
Djemili I, Aitouche A, Cocquempot V. Fault Tolerant Control of Internal Combustion Engine Subject to Intake Manifold Leakage. IFAC Proc. 2012.45(20):600-605. https://doi.org/10.3182/201208....
 
9.
Dąbrowski Z, Madej H. Masking mechanical damages in the modern control systems of combustion engines. Journal of Kones Powertrain and Transport. 2006; 13(3): 53-60.
 
10.
Dutka A, Javaherian H, Grimble M. Model-based engine fault detection and isolation. Proceedings of American Control Conference ACC '09 2009: 4593 – 4600.
 
11.
Isermann R. Model-based fault detection and diagnosis – status and applications. Annual Reviews in Control 2005; 29: 71–85.
 
12.
Nyberg M, Nielsen L. Model Based Diagnosis for the Air Intake System of the SI-Engine. SAE Technical Paper 970209. 1997. https://doi.org/10.4271/970209 .
 
13.
Komorska I, Wołczyński Z. Fault Diagnostics of Air Intake System of the Internal Combustion Engine. In book: Advances in Technical Diagnostics. Springer 2018: 91-100.
 
14.
Komorska I, Wołczyński Z, Borczuch A. Model-based analysis of sensor faults in SI engine. Combustion Engines 2017; 169(2): 146-151.
 
15.
Więcławski K, Mączak J, Szczurowski K. Fuel injector diagnostics based on observations of magnetic flux changes. Diagnostyka. 2018;19(3):89-93. https://doi.org/10.29354/diag/....
 
16.
Puchalski A, Komorska I. Data-driven monitoring of the gearbox using multifractal analysis and machine learning methods. MATEC Web of Conferences 2019; 252. https://doi.org/10.1051/matecc....
 
17.
Jack LB, Nandi AK. Fault detection using support vector machines and artificial neural networks, augmented by genetical algorithms. Mechanical Systems and Signal Processing 2002. 16(2-3): 373–390. https://doi.org/10.1006/mssp.2....
 
18.
Czech P, Łazarz B, Wilk A. Application of neural networks for detection of gearbox faults. WCEAM CM 2007. Harrogate, United Kingdom.
 
19.
Czech P, Wojnar G, Burdzik R, Konieczny Ł, Warczek J. Application of the discrete wavelet transform and probabilistic neural networks in IC engine fault diagnostics. Journal of Vibroengineering 2014; 16(4): 1619-1639.
 
20.
Strączkiewicz, Marcin & Barszcz, Tomasz. (2016). Application of Artificial Neural Network for Damage Detection in Planetary Gearbox of Wind Turbine. Shock and Vibration 2016: 1-12.
 
21.
Chen P. Study on Neural Network Automobile Fault Diagnosis Expert System. Journal of Applied Sciences 2014; 14(4);348-354.
 
22.
Ogaji SOT, Singh R. Advanced engine diagnostics using artificial neural networks. Applied Soft Computing 2003; 3(3): 259-271.
 
23.
Haykin S. Neural Networks: A Comprehensive Foundation. New York 1994. Macmillan.
 
24.
Rojas R. Neural Networks: A Systematic Introduction. New York 1994. Macmillan College Publishing Company.
 
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