Fault correction of an airflow signal in a gasoline engine system using a neural fuzzy scheme and genetic algorithm

Abstract

This study presents an airflow correction system for correcting the fault of an airflow signal of a gasoline engine. The correction system consists of a fuzzy-model-based airflow estimation system and an airflow-monitoring system. The airflow estimation system estimates the normal airflow based on fuzzy neural networks and trained using the steepest-descent method and back-propagation algorithm. The airflow-monitoring system corrects the fault airflow signal according to the correction law. In order to raise the training performance, a genetic algorithm is used to search the learning rates and initial consequent gains of the fuzzy neural networks. The estimated results indicate that using a genetic algorithm certainly improves the performance of normal airflow estimation. The corrected results indicate that the airflow correction system can provide a feasible means of carrying out airflow fault correction in gasoline engine systems.

Keywords

gasoline engine fault correction neural fuzzy air—fuel ratio

Get full access to this article

View all access options for this article.

References

Pulkrabek

W. W.

Engineering fundamentals of the internal combustion engine (Pearson Education, Prentice-Hall Upper Saddle River, New Jersey. 2004)

Matsui

Tanaka

Ohigashi

‘Measurement of local mixture strength of spark gap of S.I. engines.’ SAE paper 790483 1979, p. 88.

Gatowski

J. A.

Heywood

J. B.

Deleplace

‘Flame photographs in a spark-ignition engine.’

Combust. Flame 56 (1984): 71–81

Buehler

P. J.

Franchek

M. A.

‘Mass air flow sensor diagnostics for adaptive fueling control of internal combustion engines.’ In Proceedings of the American Control Conference, Anchorage, Alaska, USA, 8–10 May 2002, pp. 2064–2069 (IEEE, New York.

Nyberg

‘Automatic design of diagnosis systems with application to an automotive engine.’

Control Engng Practice 7 (1999): 993–1005

Nyberg

‘Using hypothesis testing theory to evaluate principles for leakage diagnosis of automotive engines.’

Control Engng Practice 11 (2003): 1263–1271

Brown

Harris

Neural fuzzy adaptive modeling and control (Prentice-Hall Englewood Cliffs, New Jersey). 1997)

Lin

C. T.

Lee

C. S. G.

Neural fuzzy system: A neuro-fuzzy synergism to intelligent systems (Simon & Schuster, Prentice-Hall Englewood Cliffs, New Jersey).1996)

Gupta

M. M.

Jin

Homma

Static and dynamic neural networks (John Wiley New York 2003)

10.

Gen

Cheng

Genetic algorithms and engineering design (New York John Wiley 1997)

11.

Pham

D. T.

Karaboga

Intelligent optimization techniques: Genetic algorithms, tabu search, simulated annealing and neural networks (New York

Springer-Verlag

2000)

12.

Angelov

P. P.

Filev

D. P.

‘An approach to on-line identification of Takagi—Sugeno fuzzy models.’

IEEE Trans. Systems, Man, Cybernetics, Part B — Cybernetics 34 (1) (2004): 484–498

13.

14.

Angelov

P. P.

Zhou

‘On line learning fuzzy rule-based system structure from data streams.’ In Proceedings of the IEEE World Congress on

Computational intelligence, 2008 IEEE International Conference on Fuzzy systems

15.

Chen

P. C.

‘Neuro-fuzzy-based fault detection of the airflow sensor of an idling gasoline engine.’

Proc. IMechE, Part D: J. Automobile Engineering 219 (2005): 1–14

16.

Takagi

Sugeno

‘Fuzzy identification of systems and its applications to modeling and control.’

IEEE Trans. Systems, Man, Cybernetics 15 (1985): 116–132

17.

Jang

J.-S. R.

Sun

C.-T.

‘Functional equivalence between radial basis function networks and fuzzy inference systems.’

IEEE Trans. Neural Networks 4 (1993): 156–159

18.

Goldber

D. E.

Genetic algorithms in search, optimization and machine learning (Reading, Massachusetts Addison-Wesley 1989)

19.

Chatterjee

Laudato

Lynch

L. A.

‘Genetic algorithms and their statistical applications: An introduction.’

Comput. Statist. Data Analysis 22 (6) (1996): 633–651

20.

Davis

Ed. Handbook of genetic algorithms (New York Van Nostrand Reinhold 1991)

21.

Gen

Cheng

Genetic algorithms and engineering design (New York John Wiley 1997)