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Benefits of Using Digital Twin for Online Fault Diagnosis of a Manufacturing System

In: Artificial Intelligence for Smart Manufacturing

Author

Listed:
  • Ramla Saddem

    (CReSTIC, University of Reims Champagne-Ardenne)

  • Dylan Baptiste

    (CReSTIC, University of Reims Champagne-Ardenne)

Abstract

In this work, we illustrate the interest in the use of a digital twin for the online fault diagnosis in a manufacturing system with sensors and actuators delivering binary signals that can be modeled as Discrete Event Systems. This chapter presents an intelligent diagnostic solution to replace traditional solutions, which are often non-industrialized, with a new data-based method learned from the simulation of the plant behaviors and using recurrent neural networks (RNN) with short-term and long-term memory (Long short-term memory, LSTM).

Suggested Citation

  • Ramla Saddem & Dylan Baptiste, 2023. "Benefits of Using Digital Twin for Online Fault Diagnosis of a Manufacturing System," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Artificial Intelligence for Smart Manufacturing, pages 255-269, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-031-30510-8_12
    DOI: 10.1007/978-3-031-30510-8_12
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