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The Application of Recurrent Neural Networks for the Diagnosis and the Prognosis of Discrete Event Systems

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The development and elaboration of monitoring approaches and tools for industrial systems are one of the major concerns for industries, as well as researchers in the field. This type of tool allows to supervise and monitor the state of operation of the system as well as to detect the possible anomalous behaviors and deviations. The work presented in these papers is part of this context. We are interested in the monitoring (Diagnosis and Prognosis) of Discrete Event Systems. However, the use of tools based on models has become a complex and exhausting task and sometimes impossible especially when it comes to large industrial systems, while the complexity of these systems has been increasing incessantly and have recently witnessed great technological progress. Therefore, the development of more sophisticated techniques is required. In this paper, we are mainly interested in the use and the exploitation of recurrent neural networks, which are a specific kind of artificial neural network that provides great dynamic behavior. To perform an intelligent approach, which will deal with the diagnosis and the prognosis of DES, so that the events generated, will be presented and analyzed by recurrent neural networks in real-time, to ensure an online diagnosis and prognosis.
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Industrial Systems; Discrete Event Systems; Monitoring; Diagnosis; Prognosis; Recurrent Neural Networks

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