ENHANCING SYSTEM AVAILABILITY IN THE PAPER INDUSTRY: A MARKOV BIRTH-DEATH APPROACH
DOI:
https://doi.org/10.11113/jm.v49.679Abstract
This study presents a methodological context for evaluating the transient-state statistical behavior of a washing system in the paper manufacturing sector. A Markov birth-death model is developed to characterize the system’s stochastic dynamics, with the dual objectives of optimizing availability and enhancing operational uptime through data-driven maintenance strategies. The system’s behavior is formalized using state transition diagrams and a set of differential equations derived for individual subsystems. These equations are computationally solved through matrix-based methods, implemented in Python programming for efficiency. In addition, a comprehensive regression analysis is conducted using SPSS software to examine the relationship between system availability and operational time. Multiple models—including linear, quadratic, exponential, and logarithmic regressions—are applied and compared to determine the best-fitting representation of availability trends. Numerical simulations reveal substantial improvements in system performance through targeted maintenance interventions, including reductions in failure rates. The results underscore the efficacy of both Markov-based modeling and regression analysis in boosting availability and operational efficiency, offering actionable insights for downtime mitigation and maintenance scheduling in industrial applications.
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