Publications Internationales

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    Design of an Advanced Optimal Fuzzy Controller For a Binary Distillation Column
    (Université M'hamed Bougara de Boumerdès, 2024) Bendib, Riad; Mechhoud, El-arkam; Bentarzi, Hamid; Batout, Naoual
    The most common control philosophy followed in the chemical process industries is the SISO system using the conventional PID controller algorithms. One drawback is relying on models for both control and design work in Chemical process industries (CPI) is that many problems are very complex and accurate models are difficult, if not impossible to obtain. To overcome these problems, it will be helpful to apply techniques that use human judgment and experience rather than precise mathematical models, which in the major cases deduced from the linearization of the system and simplification hypothesis. The fuzzy logic systems are capable of handling complex, nonlinear systems using simple solutions. However, obtaining an optimal set of fuzzy membership functions is not an easy task. In this chapter a solution based on artificial intelligence is proposed to improve the control of a binary distillation column. The solution is based on the use of the advantages of both fuzzy logic and genetic algorithms. The fuzzy logic is used as a supervisory PI controller that is a simple PI controller that generally used in controlling distillation columns with parameters deduced from the fuzzy supervisor. The membership functions shape is deduced by using research algorithms based on hierarchical genetic algorithms. The results show that the Fuzzy supervisory PI controller provide an excellent tracking toward set point change
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    Bearing fault diagnosis based on feature extraction of empirical wavelet transform (EWT) and fuzzy logic system (FLS) under variable operating conditions
    (JVE International, 2019) Gougam, Fawzi; Rahmoune, Chemseddine; Benazzouz, Djamel; Merainani, Boualem
    Condition monitoring of rotating machines has become a more important strategy in structural health monitoring (SHM) research. For fault recognition, the analysis is categorized in two essential main parts: Feature extraction and classification; the first one is used for extracting the information from the signal and the other for decision-making based on these features. A higher accuracy is needed for sensitive places to avoid all kinds of damages that can lead to economic losses and it may affect the human safety as well. In this paper, we propose a new hybrid and automatic approach for bearing faults diagnosis. This method uses a combination between Empirical wavelet Transform (EWT) and Fuzzy logic System (FLS), in order to detect and localize the early degradation of bearing state under different working conditions. EWT build a wavelet filter bank to extract amplitude modulated-frequency modulated component of signal. Modes presenting a high impulsiveness is then selected using the kurtosis indicator. Thereafter, time domain features (TDFs) are applied for the reconstructed signal to extract the fault features which are finally used as an inputs of FLS in order to identify and classify the bearing states. The experimental results shows that the proposed method can accurately extract and classify the bearing fault under variable conditions. Moreover, performance of EWT and empirical mode decomposition (EMD) are studied and shows the superiority of the proposed method
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    Intelligent detection without modeling of behavior unusual by fuzzy logic
    (Springer, 2017) Chebi, Hocine; Acheli, Dalila; Kesraoui, Mohamed
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    Fuzzy rules for joint integration of production schedule and maintenance planning
    (Old City Publishing, 2016) Berrichi, Ali; Yalaoui, Farouk; Yalaoui, Alice
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    Impulse noise reduction in 2D electrical resistivity imaging data based on fuzzy logic
    (IEEE, 2011) Ferahtia, J.; Djarfour, Noureddine; Baddari, Kamel; Khaldoun, Asmae
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    Fuzzy logic-based controller for position regulation of electric drives
    (Advances in Modelling and Analysis C, 2007) Chermalikh, A.V.; Chetate, Boukhmis; Maidanski, I.I.; Kheldoum, A.
    Electrical drives are characterized by their natural non- linearity owing to their proper design and their time-varying mathematical models. When used to drive industrial systems, e.g. variable speed or variable position drives, conventional control methods are usually applied to design speed and position controllers. However, at certain performance level, these methods are not satisfied. The present paper combines fuzzy logic, mostly used to control system characterized by non-linearity and uncertainty, with new control structures to overcome difficulties listed earlier. The obtained results have proved the good foundation of the suggested method
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    An enterprise information system agility assessment model
    (2012) Imache, Rabah; Izza, Said; Ahmed-Nacer, Mohamed
    The enterprise strategy is influenced by the environment changes: socio economic, legislative, technology, and the globalization. This makes its Information System more complex and competition increasingly fierce. In order for an enterprise to ensure its place in this hard context characterized by rapid and random changes of the internal and external environments, it must have fast adapting policy of its strategy and drive quickly important changes at all levels of its Information System in order to align it to its strategy and vice versa; that?s, it must always be agile. Therefore, agility of the Enterprise Information System can be considered as a primary objective of an enterprise. This paper deals with agility assessment in the context of POIRE project. It proposes a fuzzy logic based assessment approach in order to measure, regulate and preserve continuously the Information System agility. It also proposes a prototype implementation and an application of the proposed approach to a tour operator enterprise