Machine Learning-Driven Reliability Optimization of the 3:4::G System in Metter Industry, Rajasthan
Keywords:
Reliability optimization, Machine learning, 3:4::G system, Markov process, RPGT, Industrial maintenance, System availabilityAbstract
This study presents a machine learning-driven approach for optimizing the reliability of the 3:4::G system in Metter Industry, Rajasthan. Employing historical and real-time operational data, the research integrates the Regenerative Point Graphical Technique (RPGT), Markov modeling, and advanced machine learning algorithms to predict failures, optimize maintenance, and enhance system availability. The findings highlight the transformative potential of combining traditional reliability methods with machine learning, offering actionable insights for minimizing downtime, maximizing asset utilization, and supporting proactive maintenance in complex industrial systems.
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