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International Conference on Machine Learning, Optimization, and Data Science

LOD 2019: Machine Learning, Optimization, and Data Science pp 711–724Cite as

Conditional Anomaly Detection for Quality and Productivity Improvement of Electronics Manufacturing Systems

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11943)

Abstract

Today the integration of Artificial Intelligence (AI) solutions is part of the strategy in the industrial environment. We focus on anomaly detection in the framework of manufacturing electronic cards manufacturing under mass production conditions (24/7). Early anomaly detection is critical to avoid defects. Researches and applications of anomaly detection techniques in the industry have been published but when they face production constraints success is not guaranteed. Today’s manufacturing systems are complex and involve different behaviors. We propose and evaluate a new realistic methodology for detecting conditional anomalies that could be successfully implemented in production. The proposed solution is based on Variational Autoencoders (VAEs) which provide interesting scores under the near real-time constraints of the production environment. The results have been thoroughly evaluated and validated with the support of expert process engineers.

Keywords

  • Smart factory
  • Electronic circuit manufacturing
  • Artificial Intelligence
  • Deep conditional anomaly detection

Supported by Continental Powertrain SAS France.

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  • DOI: 10.1007/978-3-030-37599-7_59
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Acknowledgments

This work is supported by Continental Powertrain France. Special thanks goes to Minds team members: Nathalie Barbosa Roa, Alain Le Grand and Jean-Noël Tomasini.

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Authors and Affiliations

  1. IMT - Institut de Mathématiques, Université de Toulouse, CNRS UMR5219, Toulouse, France

    Eva Jabbar, Philippe Besse & Jean-Michel Loubes

  2. Continental Powertrain France SAS, Toulouse, France

    Eva Jabbar & Christophe Merle

Authors
  1. Eva Jabbar
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  2. Philippe Besse
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  3. Jean-Michel Loubes
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  4. Christophe Merle
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Corresponding author

Correspondence to Eva Jabbar .

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Editors and Affiliations

  1. University of Cambridge, Cambridge, UK

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Prof. Panos Pardalos

  3. Harvard University, Cambridge, MA, USA

    Renato Umeton

  4. Università di Catania, Catania, Catania, Italy

    Prof. Giovanni Giuffrida

  5. Almawave, Rome, Roma, Italy

    Vincenzo Sciacca

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Jabbar, E., Besse, P., Loubes, JM., Merle, C. (2019). Conditional Anomaly Detection for Quality and Productivity Improvement of Electronics Manufacturing Systems. In: Nicosia, G., Pardalos, P., Umeton, R., Giuffrida, G., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2019. Lecture Notes in Computer Science(), vol 11943. Springer, Cham. https://doi.org/10.1007/978-3-030-37599-7_59

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  • DOI: https://doi.org/10.1007/978-3-030-37599-7_59

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