Intelligence Artificielle et Data Sciences

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Browsing Intelligence Artificielle et Data Sciences by Author "Narima ALKAMA"

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    Predictive Maintenance: Review of Current Methods and Techniques
    (ESTIN, 2024) Asma BOUROUBA; Narima ALKAMA
    Achieving operational excellence is crucial for maintaining competitiveness in today’s industrial landscape. Traditional maintenance strategies, both reactive and preventive, often fail to fully utilize the abundant data available. The emergence of Industry 4.0, emphasizing data acquisition and analytics, has introduced predictive maintenance, allowing for real-time insights into equipment health and proactive interventions. Business Intelligence (BI) systems play a central role in this shift by converting raw data into actionable insights, facilitating informed decision-making. This work examines the integration of Natural Language Processing (NLP), probabilistic models, and machine learning techniques in predictive maintenance, offering a comprehensive review of current methodologies. The research highlights how these advanced technologies can improve equipment reliability, reduce downtime, and optimize resource allocation, thereby enhancing production efficiency and profitability. The findings support a transition from traditional maintenance approaches to more proactive strategies, aligning with Industry 4.0 goals and fostering a data-driven, automated industrial environment
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    Text Mining for Predictive Maintenance Case Study: Cevital agro-industry
    (Tassadit, 2025-01-21) Asma BOUROUBA; Narima ALKAMA
    Achieving operational excellence is vital for maintaining competitiveness in today’s in dustrial landscape. Traditional maintenance strategies, both reactive and preventive, often fail to fully leverage the extensive data available. The advent of Industry 4.0, with its fo cus on data acquisition and analytics, has led to the development of predictive maintenance, enabling real-time insights into equipment health and proactive interventions. This thesis introduces an innovative approach to predictive maintenance by applying NLP techniques to textual maintenance logs. Our method utilizes BERTopic for embedding, K-means cluster ing for grouping intervention descriptions, and autoencoders for feature extraction to identify equipment degradation states. These states are then used to develop a Bayesian network based predictive model. Applied to Cevital, an Algerian agri-food conglomerate using Coswin 8i, our approach aims to enhance maintenance planning and operational efficiency. The clus tering model achieved a high silhouette score of 97%, and by integrating it with the Bayesian model, we attained an accuracy of 83%. This study highlights the potential of integrating advanced NLP and predictive analytics into maintenance management. This work underscores the transformative potential of integrating cutting-edge NLP tech niques and predictive analytics into traditional maintenance management practices. By har nessing textual data effectively, organizations can transition from reactive to proactive main tenance strategies, thereby minimizing downtime, reducing operational costs, and ultimately enhancing overall productivity and competitiveness in the industry.