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

Failure Prediction of Highly Requested Complex Technical Systems: Application to W18v50df Engines

The work done in this paper has focused on the prediction of failures of a complex and highly stressed technical system for energy production, namely the W18V50DF engine powering an MW gas-fired power plant. The aim of this work is to highlight the prediction curves, a priori and a posteriori, of the evolution (probabilistic) of the state of these W18V50DF engines in order to anticipate the appearance of failure and to put a human-machine interface to facilitate the knowledge of a possible event and to allow a remote action. To do this, a hybrid method has been employed in the field of data-oriented modeling which highlights the neural network modeling used to determine the state of the components of the system studied by classification. Coupled with Bayesian network modeling, also known as probabilistic graphical models used to predict the state of the system. The neural model and the HMI have been built respectively through the ntools library and via the GUIDE library of the MATLAB software, while the probabilistic graphical model has been built using the BayesianLab software. The work carried out has shown that the W18V50DF engine and its components are degraded as their lifetimes evolve. In addition, because of its complexity and the criticality of some of its components, the degradation of the W18V50DF engine will be accelerated as each of them will be. In addition to this, the financial evaluation revealed that this work, beyond its multiple technical challenges, would allow the user to make significant financial gains

Modelling an Interactive Road Signs System, Using Petri Nets

This paper is a contribution to the problems of road insecurity in Africa. Due to non-respect of road sign and to the lack of signing, roads have become places of all dangers. It becomes imperative to establish an interaction between the authorities and the offending drivers. To reach this goal, we modelled an interactive road-vehicle-signage system, who locally informs the driver on the requirements of traffic signs. This model having interest only in the event of driving by bad weather or deterioration of panels, we are amending by inserting functions aimed to warn and punish the driver in the event of maintenance of an offense. Indeed, when the driver is about to commit a fault, firstly the system issues a warming (visual, audible or mechanical). Then, a message (SMS) is sent to the authorities. We include the concept of floating process engaged by devices other than the signage. We show that, with a few considerations, from the functional point of view, they are identical to the process engaged by the signage. Furthermore, in terms of performance, the model renewed warnings that occurred just before the end panel of prohibitions. It stores messages of offenses occurred without the network, then notifies them when a network is detected. We propose algorithms for incremental design and analysis of the model, whose processes are activated and / or are extinguished, according to the type of sign or tag encountered. We show by simulation and by linear algebra that, the model retains its properties of absence of blocking and boundedness during the evolution of the system, hence its validation

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