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Neural Network Classification of Diesel Spray Images

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4251))

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Abstract

This paper describes an evaluation of a neural network technique for modelling fuel spray penetration in the cylinder of a diesel internal combustion engine. The model was implemented using a multi-layer perceptron neural network. Two engine operating parameters were used as inputs to the model, namely injection pressure and in-cylinder pressure. Spray penetration length were modelled on the basis of these two inputs. The model was validated using test data that had not been used during training, and it was shown that semi-automated classification of complex diesel spray data is possible. The work lays the foundations for the establishment of an improved neural network paradigm for totally automatic, fast, accurate analysis of such complex data, thus saving many man-hours of tedious manual data analysis.

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Author information

Authors and Affiliations

  1. Engineering Research Centre, School of Engineering, The University of Brighton, Brighton, U.K.

    S. D. Walters, S. H. Lee, C. Crua & R. J. Howlett

Authors
  1. S. D. Walters
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  2. S. H. Lee
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  3. C. Crua
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  4. R. J. Howlett
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Editor information

Editors and Affiliations

  1. School of Design, Engineering and Computing, Bournemouth University, UK

    Bogdan Gabrys

  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Lakhmi C. Jain

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© 2006 Springer-Verlag Berlin Heidelberg

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Walters, S.D., Lee, S.H., Crua, C., Howlett, R.J. (2006). Neural Network Classification of Diesel Spray Images. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_142

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  • DOI: https://doi.org/10.1007/11892960_142

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46535-5

  • Online ISBN: 978-3-540-46536-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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