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Residual generation for fault detection of internal combustion engine

Published: 16 June 2011 Publication History

Abstract

Fault detection (FD) scheme is developed for automotive engines in this paper. The method uses an independent Radial Basis Function (RBF) Neural Network model to model engine dynamics, and the modelling errors are used to form the basis for residual generation. The method is developed and the performance assessed using the engine benchmark, the Mean Value Engine Model (MVEM) with Matlab/Simulink. Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The simulation results showed that all the simulated faults can be clearly detected in the dynamic condition throughout the operating range.

References

[1]
Isermann, R. Model-based fault-detection and diagnosis status and applications, Annual Reviews in Control 29, pp. 71--85,2005.
[2]
Capriglione, D., and Pietrosanto, A. Real-Time Implementation of IFDIA Scheme in Automotive System. IEEE Transactions on Instrumentation and Measurement, vol. 56, No. 3, pp.1667--1672, June 2004.
[3]
Wang, S. W., Yu, D. L., Gomm, J. B., G. F. Page and S. S. Douglas. Adaptive neural network model based predictive control for air- fuel ratio of SI engines. Engineering Applications of Artificial Intelligence 19, Elsevier, pp. 189--200, 2006.
[4]
Elbert, H. Donn, E. and Fam, M. A Generic Mean Value Engine Model for Spark Ignition Engines. In proceeding of 41st simulation conference, Denmark. DTU Lyngby,2000.

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cover image ACM Other conferences
CompSysTech '11: Proceedings of the 12th International Conference on Computer Systems and Technologies
June 2011
688 pages
ISBN:9781450309172
DOI:10.1145/2023607
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • TELECVB: TELECOMS - Varna, Bulgaria
  • Austrian Comp Soc: Austrian Computer Society
  • BPCSB: BULGARIAN PUBLISHING COMPANY - Sofia, Bulgaria
  • IOMAIBB: INSTITUTE OF MATHEMATICS AND INFORMATICS - BAS, Bulgaria
  • NBUBB: New Bulgarian University - BAS, Bulgaria
  • Technical University of Sofia
  • IOIACTBB: INSTITUTE OF INFORMATION AND COMMUNICATION TECHNOLOGIES - BAS, Bulgaria
  • TSFPS: THE SEVENTH FRAMEWORK PROGRAMME - SISTER
  • ERSVB: EURORISC SYSTEMS - Varna, Bulgaria
  • FOSEUB: FEDERATION OF THE SCIENTIFIC ENGINEERING UNIONS - Bulgaria
  • UORB: University of Ruse, Bulgaria
  • BBPSB: BULGARIAN BUSINESS PUBLICATIONS - Sofia, Bulgaria
  • CASTUVTB: CYRIL AND ST. METHODIUS UNIVERSITY of Veliko Tarnovo, Bulgaria
  • TECHUVB: Technical University of Varna, Bulgaria
  • LLLPET: LIFELONG LEARNING PROGRAMME - ETN TRICE
  • IEEEBSB: IEEE Bulgaria Section, Bulgaria

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 June 2011

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Author Tags

  1. RBF neural networks
  2. automotive engines
  3. fault detection
  4. independent RBFNN model

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  • Research-article

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CompSysTech '11
Sponsor:
  • TELECVB
  • Austrian Comp Soc
  • BPCSB
  • IOMAIBB
  • NBUBB
  • IOIACTBB
  • TSFPS
  • ERSVB
  • FOSEUB
  • UORB
  • BBPSB
  • CASTUVTB
  • TECHUVB
  • LLLPET
  • IEEEBSB

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Overall Acceptance Rate 241 of 492 submissions, 49%

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