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Communication Dans Un Congrès Année : 2019

Reliability and maintenance cost forecasting for systems with multistate components using artificial neural networks

Phuc Do Van
Benoît Iung

Résumé

In this paper, a study on the use of artificial neural networks for predicting the system reliability and maintenance cost of a system with multistate components is presented. TensorFlow and Keras APIs are used to API to build and train deep learning models under Python environment. Different numerical experimentations are carried out to illustrate the use of the robustness of the prediction approach. The obtained results show that artificial neural networks with TensorFlow and Keras APIs are a relevant tool for reliability and maintenance cost prediction.
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Dates et versions

hal-02337912 , version 1 (29-10-2019)

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  • HAL Id : hal-02337912 , version 1

Citer

Phuc Do Van, Benoît Iung, Cristiano Cavalcante. Reliability and maintenance cost forecasting for systems with multistate components using artificial neural networks. 4th IEEE International Conference on System Reliability and Safety, ICSRS2019, Nov 2019, Rome, Italy. ⟨hal-02337912⟩
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