An Improvement of Faults Diagnosis and Prognosis of Discrete Event Systems Based on Reliability Laws

2019 ◽  
Vol 45 ◽  
pp. 148-155
Author(s):  
Mohammed Msaaf ◽  
Fouad Belmajdoub
Keyword(s):  
Fuzzy Logic ◽  
Life Cycle ◽  
Failure Probability ◽  
Discrete Event Systems ◽  
Discrete Event ◽  
Diagnosis And Prognosis ◽  
Event Systems ◽  
Faults Diagnosis

In this work, we are interested in the faults diagnosis and the faults prognosis in discrete event systems described by sequences of generated events. Through this work, we aim the maximization of the efficiency of diagnosis/prognosis operations by combining two concepts. The first one is the approach already developed in one of our works which consider the k-last generated events to perform the diagnosis/prognosis. The second concept is the reliability that takes into consideration the life cycle of each component of the discrete event systems to give the failure probability. This combination will be made using some notions of fuzzy logic.

scholarly journals Discrete Event Systems Fault’s Diagnosis and Prognosis Using Feed-Forward Neural Networks

2021 ◽  
Vol 54 (6) ◽  
pp. 853-863
Author(s):  
Amri Omar ◽  
Fri Mohamed ◽  
Msaaf Mohammed ◽  
Belmajdoub Fouad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Discrete Event Systems ◽  
Discrete Event ◽  
Modeling Tools ◽  
Feed Forward ◽  
Industrial Systems ◽  
Diagnosis And Prognosis ◽  
Event Systems ◽  
Fault Diagnosis And Prognosis

The elaboration and development of monitoring (diagnostic and prognostic) tools for industrial systems has been one of the main concerns of the researchers for many years, so that many researches and studies have been developed and proposed, especially concerning discrete event systems (DES), which occupy an important class of industrial systems. However, the use of modeling tools to ensure these operations become a complex and exhausting task, while the complexity of industrial systems has been increasing incessantly. Therefore, the development of more and more sophisticated techniques is required. In this context, the use of artificial neural networks (NN) seems interesting, because thanks to their automatics and intelligent algorithms, the NN could handle perfectly DES diagnosis and prognosis problems. For this purpose, in the following papers, we propose an intelligent approach based on feed-forward neural network, which will deal with fault diagnosis and prognosis in DES, so that the events generated by the DES, will be presented and analyzed by the neural network in real-time, in order to perform an online diagnosis and prognosis.

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