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).
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Saddem, R., Baptiste, D. (2023). Benefits of Using Digital Twin for Online Fault Diagnosis of a Manufacturing System. In: Tran, K.P. (eds) Artificial Intelligence for Smart Manufacturing. Springer Series in Reliability Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-30510-8_12
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DOI: https://doi.org/10.1007/978-3-031-30510-8_12
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