Text Mining for Predictive Maintenance Case Study: Cevital agro-industry

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dc.contributor.authorAsma BOUROUBA
dc.contributor.authorNarima ALKAMA
dc.date.accessioned2025-01-21T20:50:58Z
dc.date.available2025-01-21T20:50:58Z
dc.date.issued2025-01-21
dc.description.abstractAchieving 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.
dc.identifier.urihttps://dspace.estin.dz/handle/123456789/27
dc.language.isoen
dc.publisherTassadit
dc.subjectPredictive Maintenance
dc.subjectMaintenance Logs
dc.subjectArtificial Intelligence (AI)
dc.subjectData Analytics
dc.subjectNatural Language Processing (NLP)
dc.subjectText Mining
dc.subjectBayesian Network
dc.subjectEquipment Degradation
dc.titleText Mining for Predictive Maintenance Case Study: Cevital agro-industry
dc.typeThesis

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Projet de Fin d'Études : Mémoire d'Ingénieur