Génie Eléctriques

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Detection and identification of defects in gearbox systems using artificial intelligence based techniques
(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2023) Ikhlef, Boualem; Benazzouz, Djamel(Directeur de thèse)
Gearboxes are massively utilized in nowadays industries due to their huge importance in power transmission; hence, their defects can heavily affect the machines performance. Therefore, many researchers are working on gearboxes fault detection and classification. However, most of the works are carried out under constant speed conditions, while gears usually operate under varying speed and torque conditions, making the task more challenging. In this work, we propose a new method for gearboxes condition monitoring that is efficiently able to reveal the fault from the vibration signatures under varying operating condition. First, the vibration signal is processed with the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) to extract the modes. Next, time domain features are calculated from each mode. Then the features set are reduced using the Ant colony optimization algorithm (ACO) by removing the redundant and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm Random Forest (RF) is used to train a model able to classify the fault based on the selected features. The innovative aspect about this method is that, unlike other existing methods, ACO is able to optimize not only the features but also the parameters of the classifier in order to obtain the highest classification accuracy. The proposed method was tested on varying operating condition real dataset consisting of six different gearboxes. In the aim to prove the performance of our method, it had been compared to other conventional methods. The obtained results indicate its robustness, and its accuracy stability to handle the varying operating condition issue in gearboxes fault detection and classification with high efficiency
Keratoconus prognosis study for patients with corneal external mechanical stress mode
(Université M'Hamed Bougara : Faculté de Technologie, 2021) Bettahar, Toufik; Benazzouz, Djamel(Directeur de thèse)
A corneal numerical model was developed based on experimental previous studies, and gathered data from EKBASSIRA eye clinic. The cornea is considered a two layers 3D viscoelastic solid. Uniaxial and multiaxial cyclic loads has been applied on the Keratoconus and healthy corneas in order the emphasize the influence of eye rubbing on corneal applanation, loss of shape and biomechanical properties .A Finite element analysis simulation results have shown a significant difference between healthy and KC stages responses to those external loads, for a fixed intraocular pressure .Life time prognosis and classification algorithms using analytical life time computation and Artificial Neural Network approaches was then established to shed light on the severity of eye rubbing on the evolution and the progression rate of Keratoconus and its offset, for patients with subclinical, mild and advanced forms of the disease
Energy consumption modelling of marine drones and the integration of the model into ROS-based simulation
(Université M'Hamed Bougara : Faculté de Technologie, 2021) Touzout, Walid; Benazzouz, Djamel(Directeur de thèse)
The Unmanned Surface Vehicles (USVs) are promising solutions for various marine applications such as: maritime navigation, rescue, environmental control, military missions, oceanic maps production, etc. The main advantage of USVs is the ability to execute their functionalities in environments where humans are not able to intervene safely, in addition to their cost and continuous activity. Generally, USVs operate in difficult environmental conditions requiring precision, reliability, and autonomy. To meet these critical requirements, the scientific community is increasingly focusing its research in the USV’s field and their applications. Accordingly, one of the most difficult issues to be resolved in this field is the autonomy and energy limitation problems. Estimating and managing the power consumption of USVs is an important issue to deal with energy minimization techniques such as trajectory planning, task scheduling and optimal design of controllers. In this thesis, we present the energy consumption parameter of USVs into Robot Operating System (ROS) - based simulation through the following contributions: • An analytical model of the energy consumption of differential drive Unmanned Surface Vehicles is developed based on a three-degrees-of-freedom dynamic model of surface vessels. • A reverse engineering approach is proposed allowing the identification of the developed dynamic model’s coefficients and parameters based on a set of scenarios run within the simulation environment presented in [1]. The identified model is used in the development of the consumption model of surface vehicles. • The simulator engine is enriched with power modelling and simulation tools, so that the power consumed by the USV is instantaneously calculated, processed, and returned; thus, the energy required to accomplish a given predefined scenario is available as a new simulation result

Génie Eléctriques

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