Asma BOUROUBANarima ALKAMA2024-12-102024-12-102024https://dspace.estin.dz/handle/123456789/21In conclusion, this dissertation emphasizes the transformative impact of predictive maintenance within the framework of Industry 4.0, focusing on the integration of advanced data analytics to enhance operational excellence in industrial settings. The research demonstrates that by leveraging Natural Language Processing (NLP), probabilistic models, and machine learning techniques, industries can transition from traditional reactive and preventive maintenance strategies to more proactive and predictive approaches. This shift ensures not only higher equipment reliability and availability but also minimizes downtime and associated costs, ultimately boosting production efficiency and profitability. The comprehensive review of existing research studies and methodologies within this dissertation illustrates the effectiveness of these advanced technologies in analyzing both structured and unstructured data for predictive maintenance. The ability to process and interpret large volumes of data allows for the early detection of potential equipment failures, enabling timely and informed decision-making. This proactive approach to maintenance not only enhances the lifespan of industrial assets but also optimizes resource allocation and reduces unexpected disruptions in production processes. Furthermore, the insights gained from this research provide a robust framework for implementing predictive maintenance strategies, paving the way for more intelligent and efficient industrial operations. The adoption of such advanced maintenance techniques aligns with the goals of Industry 4.0, promoting a more data-driven and automated industrial environment.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 environmentenThesis