A Proposed Roadmap for Optimizing Predictive Maintenance of Industrial Equipment

Abstract

Now-a-days, the maintenance management of industrial equipment, particularly in the aeronautical industry, has evolved into a substantial challenge and a critical concern for the sector. Aeronautical wiring companies are currently grappling with escalating difficulties in equipment maintenance. This paper proposes an intelligent system for the automated detection of machine failures. It assesses predictive maintenance approaches and underscores the significance of sensor selection to optimize outcomes. The integration of Machine Learning techniques with the Industrial Internet of Things (IIoT) and intelligent sensors is presented, showcasing the heightened accuracy and effectiveness of predictive maintenance, especially in the aeronautical industry. The research aims to leverage Predictive Maintenance for enhancing the performance of production machines, predicting their failures, recognizing faults, and determining maintenance dates through the analysis and processing of collected data. Employing sophisticated code, the study emphasizes real-time data collection, data traceability, and enhanced precision in predicting potential failures using Machine Learning. The findings underscore the collaboration between sensors and the synergy of Machine Learning with IIoT, ultimately aiming for sustained reliability and efficiency of predictive maintenance in aeronautical wiring companies.