Proposal of a Machine Learning Predictive Maintenance Solution Architecture


  • Aurelia Pătrașcu Department of Cybernetics, Economic Informatics, Finance and Accounting, Petroleum-Gas University of Ploiesti, Romania
  • Cristian Bucur Department of Cybernetics, Economic Informatics, Finance and Accounting, Petroleum-Gas University of Ploiesti, Romania
  • Ana Tănăsescu Department of Cybernetics, Economic Informatics, Finance and Accounting, Petroleum-Gas University of Ploiesti, Romania
  • Florentina Alina Toader Department of Cybernetics, Economic Informatics, Finance and Accounting, Petroleum-Gas University of Ploiesti, Romania



predictive maintenance, machine learning techniques, platform architecture, data-driven approach, anomaly detection, Industry 4.0


This article proposes an architecture model for a predictive maintenance solution that can be used to detect degradation of equipment in industrial units. This platform is compliant with Industry 4.0 standards and employs machine-learning algorithms alongside with data analytics methodologies to model the kinetics of equipment degradation. This serves the overarching aim of Industry 4.0 by enabling real-time, data-driven decision-making and complex asset management. To model the deterioration of the equipment, advanced data analysis techniques and machine learning were used, thus allowing for the early identification of imminent failures and reducing system downtime. The proposed solution was validated through numerous experiments and a comprehensive analysis of data. The results indicate not only enhanced operational reliability but also a reduction in environmental impact, thereby highlighting the value of intersecting Industry 4.0 and Sustainability paradigms in the field of industrial systems.


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