Data-Driven Decision Modelling for Predictive Maintenance Optimization in Mechanical Systems
DOI:
https://doi.org/10.5281/zenodo.21910675Keywords:
Data-Driven Modeling (DDM), Predictive Maintenance optimization (PMO), Mechanical System (MS)Abstract
Data-driven decision-making will play an important role in predicting the maintenance of mechanical systems regarding Industry 4.0 and modern smart manufacturing systems. This study communicates the benefits of using an integrated framework for maintenance scheduling, which aids in decreasing the downtime of mechanical systems and ultimately increasing their operating efficiency. The proposed framework uses condition monitoring data to maintain real-time system correctness while attempting to predict faults in system behaviour. To determine the optimum system maintenance approach, this research adopts a combination of decision and learning models. The framework includes cost-oriented maintenance that accounts for production and maintenance losses in mechanical systems. Pre- and post-processed data, along with advanced time series of operation degradation of mechanical systems, constitute the framework methodologies. Simulation of the proposed framework demonstrates that predictive capabilities, unplanned downtime, and operating efficiency increase with the framework, in contrast to reactive and preventive maintenance systems. The proposed framework is dynamic and allows operating systems to incorporate changes and system operational uncertainty. The proposed framework supports data-driven, scalable predictive systems in modern industry to enhance system maintenance. The inactivity prediction, maintenance decision support, and maintenance system aids receive motivation to enhance systems' predictive maintenance operations.
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