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Engineering Management for Fault Detection System Using Convolutional Neural Networks

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Proceedings of the Fifteenth International Conference on Management Science and Engineering Management (ICMSEM 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 78))

Abstract

In recent years, due to the increase in computational power, the field of artificial intelligence focused on image classification has undergone a great development. Classical pattern-based methods have serious problems in achieving effective algorithms that fail as less as possible, and are often heavily influenced by environmental variables such as light, changes in the image format, etc. Convolutional Neural Networks are particularly effective as image classifiers, and a good solution to this problem, but they do not completely close the issue. An image classifier indicates the elements, in this case, defects within an image that contain a piece or component but does not give more information. In this work, an implementation of a hybrid neural network system based on two neural networks is proposed. The output of the first one feeds the input of the second one, so that a more robust and efficient algorithm is generated. The combination of an image classifier with linear classifiers will be studied in order to obtain a decision on a real manufacturing part.

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Acknowledgements

The work reported herewith has been financially by the Universidad Europea de Madrid and the Dirección General de Universidades, Investigación e Innovación of Castilla-La Mancha, under Research Grant ProSeaWind project (Ref.: SBPLY/19/180501/000102). The authors are thankful to Defta Spain S.L.U. company.

Author information

Authors and Affiliations

  1. Universidad Europea de Madrid, Villaviciosa de Odón, Spain

    Diego Ortega Sanz & Carlos Quiterio Gomez Muñoz

  2. Ingenium Research Group, University of Castilla-La Mancha, Ciudad Real, Spain

    Fausto Pedro García Márquez

Authors
  1. Diego Ortega Sanz
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  2. Carlos Quiterio Gomez Muñoz
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  3. Fausto Pedro García Márquez
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Corresponding author

Correspondence to Fausto Pedro García Márquez .

Editor information

Editors and Affiliations

  1. Business School, Sichuan University, Chengdu, China

    Jiuping Xu

  2. University of Castilla-La Mancha (UCLM), Ciudad Real, Ciudad Real, Spain

    Fausto Pedro García Márquez

  3. World Academy of Sciences, Trieste, Italy

    Mohamed Hag Ali Hassan

  4. Academy of Sciences of Moldova, Chisinau, Moldova

    Gheorghe Duca

  5. Institute of Systems Control, Azerbaijan National Academy of Sciences, Baku, Azerbaijan

    Asaf Hajiyev

  6. Department of Industrial Engineering, Gazi University, Ankara, Turkey

    Fulya Altiparmak

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Ortega Sanz, D., Gomez Muñoz, C.Q., García Márquez, F.P. (2021). Engineering Management for Fault Detection System Using Convolutional Neural Networks. In: Xu, J., García Márquez, F.P., Ali Hassan, M.H., Duca, G., Hajiyev, A., Altiparmak, F. (eds) Proceedings of the Fifteenth International Conference on Management Science and Engineering Management. ICMSEM 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-79203-9_28

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