Evaluation of the Performance of Telecommunication Systems by Approach of Hybrid Stochastic Automata Combined With Neuro-Fuzzy Networks

This paper presents a functional and dysfunctional behavioral study of a telecommunication system, with the aim to evaluate the performance of its constituent units. It is question of taking advantage offered by artificial intelligence in order to evaluate by modeling and simulation in system reliability. The methodological approach consists in combining ANFIS neuro-fuzzy networks with hybrid stochastic automata. The Neuro-Fuzzy ANFIS networks provide a prediction for the passage from nominal mode to degraded mode, by controlling the occurrence of malfunctions at transient levels. This allows to anticipate the occurrence of events degrading system performance, such as failures and disturbances. The objective is to maintain the system in nominal operating mode and prevent its tipping in degraded mode. The results are implanted around a demonstrator based on Scilab, and implemented on Matlab / Simulink

Management of Hybrid Renewable Energy Systems Using Differential Hybrid Petri Nets

Investigations in this paper concern management of Hybrid Renewable Energy systems. To achieve it, a supervisory system based on hybrid systems concept is designed, in order to ensure power flow between energy generators (solar panel and pico-hydroelectric), batteries and load. Differential Hybrid Petri Net is used to model the proposed supervisory and simulations are made in Matlab environment.Results obtained present a good performance criteria Loss of Power Supply Probability, and this show the effectiveness of our approach in the coordination of HREs components during the energy sharing process by reducing load shedding in microgrid system

Modeling and Fault Detection of A Turbofan Engine by Deep-learning Approach

Throughout the world, thousands of passengers travel by air, their quality depends on that of the equipment used. Predictive maintenance is increasingly used to estimate. The remaining useful life of system components and in particular turbofan engines as an essential component. It is used to predict failure before it occurs, optimize component design, extend equipment life, and reduce maintenance costs. However, the algorithms proposed in the literature to date to determine the remaining useful life lack precision with a quadratic error around 20 while the physical models have errors of the order of 0.02. The problem here is how to increase the accuracy of predicting the remaining useful life of a turbofan engine. The objective of this study is to develop a more realistic and accurate algorithm for calculating the remaining useful life of a turbofan engine. To do this, we considered the degradation of the high pressure compressor and the fan as essential organs of the turbojet engine and we used deep learning, known for its high precision linked to a great capacity for extracting information. More specifically, it involved acquiring data on a turbojet engine in operation, pre-processing this data, developing the prediction model, training the model and finally validating the approach in comparison with other diagnostic methods. and to model these defects. We compared two deep learning architectures per application against the CMAPSS dataset to assess their performance. The LSTM architecture we developed prevailed with an RMSE of 13.76, well positioned compared to the literature architecture