Browsing by Author "Moussaoui, Imane"

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    Analysis of bearings defaults using machine learning techniques
    (Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2025) Moussaoui, Imane; Benazzouz, Djamel(Directeur de thèse)
    Rotating machines have become ubiquitous in contemporary industries, playing a pivotal role in various applications. The consequences of defects in these machines extend beyond mere technical issues, potentially leading to substantial economic losses and posing a significant threat to human safety. Operators often grapple with the intricacies of troubleshooting these complex systems, where a single mistake can have catastrophic consequences. One of the most critical elements in these machines is the bearings. Consequently, numerous researchers have dedicated their time and efforts to addressing this matter. While extensive studies have been conducted in this field, a common limitation is the focus on constant-speed scenarios. In reality, rotating machines typically operate under non-stationary conditions, making constant-speed techniques largely theoretical. This thesis is divided into two essential parts. The first part addresses the challenges of diagnostic resolution under time-varying conditions. Given the dynamic nature of the working environment, understanding and mitigating faults in non-stationary conditions is imperative for practical applications. Our method aims to tackle the diagnostic issue under time-varying conditions. The technique was tested on a bearing database collected under time-varying conditions, containing three types of faults. Vibrational signals are initially processed using the Empirical Wavelet Transform (EWT) to extract AM-FM modes. Subsequently, a list of features is extracted from these modes. For feature selection, the Clan-Based Cultural Algorithm (CCA) is employed, and model training utilizes the Random Forest algorithm. The results demonstrate the robustness of the diagnostic process despite varying conditions. The second part focuses on feature selection, which plays a crucial role in controlling the quality of the diagnostic system and reducing misleading factors. This area of research is increasingly attracting attention, with numerous methods developed. However, many of these techniques require in-depth domain knowledge, particularly concerning parameter tuning and result interpretation. In this work, we introduce a robust technique based on standard deviation and Random Forest methods for sequential feature selection. The method was tested on three different bearing databases, including time-varying conditions, and three signal decomposition techniques (EWT, EMD, and MODWPT). It provided promising results in terms of both quality and quantity, being user-friendly and not demanding extensive knowledge in the optimization field
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    Bearing fault detection under time-varying speed based on empirical wavelet transform, cultural clan-based optimization algorithm, and random forest classifier
    (SAGE Publications, 2021) Moussaoui, Imane; Rahmoune, Chemseddine; Zair, Mohamed; Benazzouz, Djamel
    Bearings are massively utilized in industries of nowadays due to their huge importance. Nevertheless, their defects can heavily affect the machines performance. Therefore, many researchers are working on bearing fault detection and classification; however, most of the works are carried out under constant speed conditions, while bearings usually operate under varying speed conditions making the task more challenging. In this paper, we propose a new method for bearing condition monitoring under time-varying speed that is able to detect the fault efficiently from the vibration signatures. First, the vibration signal is processed with the Empirical Wavelet Transform to extract the AM-FM modes. Next, time domain features are calculated from each mode. Then, the features’ set is reduced using the Cultural Clan-based optimization algorithm by removing the redundant and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm “Random Forest” is used to train a model able to classify the fault based on the selected features. The proposed method was tested on a time-varying real dataset consisting of three different bearing health states: healthy, outer race defect, and inner race defect. The obtained results indicate the ability of our proposed method to handle the speed variability issue in bearing fault detection with high efficiency
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    Implementation of determinisic and probabilisic fiber tracking algorihms for abnormal brain tissues analysis using dMRI
    (Université M’Hamed BOUGARA de Boumerdes : Institut de génie electrique et electronique (IGEE), 2019) Moussaoui, Imane; Cherifi, Dalila (supervisor)
    Diffusion magnetic resonance imaging (DMRI) is a technique that allows to probe the microstructure of materials. In our case we use it for the White Matter (WM) while tractography is a computational reconstruction method based on diffusion-weighted mag- netic resonance imaging (DWI)that attempts to reveal the trajectories of white matter pathways in vivo and to infer the underlying structural connectome of the human brain. The aim of our study is to reach the best reconstruction of the WM in the presence of abnormal tissues such as Astrocytoma type II and III, Glioblastoma Multiform, Menin- gioma and Oligodendrocytoma type II. For that purpose, nine data about the mentioned diseases aquired from the the UK data archive are utilised, the procedure is to apply both deterministic and probabilistic methods with two stopping criteria for each to the dataset. The analysis of the four outputs is conducted for each patient to assess the results in the region of interest (ROI). Besides the comparison between the tracts generated with the probabilistic and the deter- ministic algorithms, another comparison is performed for FA=0.2 and FA=0.4 as stopping criteria and their effect on the generated fibers. The main contribution of this work is the implementation of the probabilistic tracking algorithm. While searching for information concerning tractography .It is found that de- terministic tractography is widely used because of its ease and simplicity. In this repport advantages of using the probabilistic method for better results demonstrated therefore both methods were applied on the same dataset in addition to analising the effect of stopping criterion on the results in the ROI and the whole brain.
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    Implementation of determinisic and probabilisic fiber tracking algorihms for abnormal brain tissues analysis using dMRI
    (2019) Moussaoui, Imane; Cherifi, Dalila (supervisor)
    Diffusion magnetic resonance imaging (DMRI) is a technique that allows to probe the microstructure of materials. In our case we use it for the White Matter (WM) while tractography is a computational reconstruction method based on diffusion-weighted mag-netic resonance imaging (DWI)that attempts to reveal the trajectories of white matter pathways in vivo and to infer the underlying structural connectome of the human brain. The aim of our study is to reach the best reconstruction of the WM in the presence of abnormal tissues such as Astrocytoma type II and III, Glioblastoma Multiform, Menin- gioma and Oligodendrocytoma type II. For that purpose, nine data about the mentioned diseases aquired from the the UK data archive are utilised, the procedure is to apply both deterministic and probabilistic methods with two stopping criteria for each to the dataset. The analysis of the four outputs is conducted for each patient to assess the results in the region of interest (ROI). Besides the comparison between the tracts generated with the probabilistic and the deter- ministic algorithms, another comparison is performed for FA=0.2 and FA=0.4 as stopping criteria and their effect on the generated fibers. The main contribution of this work is the implementation of the probabilistic tracking algorithm. While searching for information concerning tractography .It is found that de-terministic tractography is widely used because of its ease and simplicity. In this repport advantages of using the probabilistic method for better results demonstrated therefore both methods were applied on the same dataset in addition to analising the effect of stopping criterion on the results in the ROI and the whole brain.
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    Rolling bearing fault feature selection based on standard deviation and random forest classifier using vibration signals
    (SAGE, 2023) Moussaoui, Imane; Rahmoune, Chemseddine; Benazzouz, Djamel
    The precise identification of faults is vital for ensuring the reliability of the bearing’s performance, and thus, the functionality of rotary machinery. The focus of our study is on the role that feature selection plays in improving the accuracy of predictive models used for diagnosis. The study combined the Standard Deviation (STD) parameter with the Random Forest (RF) classifier to select relevant features from vibration signals obtained from bearings operating under various conditions. We utilized three databases with different bearings’ health states operating under distinct conditions. The results of the study were promising, indicating that the proposed method was not only effective but also consistent, even under time-varying conditions