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<p>Ensuring the reliability of critical infrastructure systems is essential for industrial and agricultural sustainability. This study focuses on analyzing the reliability of two interconnected systems: a centrifugal pumping system, comprising an impeller and shaft, and a state water supply system, operating in parallel. The research employs a semi-Markov process and the regenerative point technique to evaluate key reliability metrics, including Mean Time to System Failure (MTSF), availability, and profitability. The failure rates are assumed to be constant, and the repair times follow an exponential distribution. The results reveal that parallel configurations significantly enhance reliability, reduce downtime, and improve resource allocation efficiency. Furthermore, the findings demonstrate that increasing repair rates improve MTSF, availability, and overall profitability. It also shows that the state water supply system showing greater sensitivity to repair rate variations. This study contributes a robust analytical framework for optimizing maintenance schedules, minimizing costs, and supporting decision-making. Future work will focus on integrating advanced machine learning techniques and simulation models to further validate and enhance system performance predictions.</p>

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Enhancing sustainability through reliability optimization of interconnected pump and water supply systems using a semi-Markov model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月29日

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  • Yogita Rani,
  • Gitanjali Sharma,
  • Indeewar Kumar

摘要

Ensuring the reliability of critical infrastructure systems is essential for industrial and agricultural sustainability. This study focuses on analyzing the reliability of two interconnected systems: a centrifugal pumping system, comprising an impeller and shaft, and a state water supply system, operating in parallel. The research employs a semi-Markov process and the regenerative point technique to evaluate key reliability metrics, including Mean Time to System Failure (MTSF), availability, and profitability. The failure rates are assumed to be constant, and the repair times follow an exponential distribution. The results reveal that parallel configurations significantly enhance reliability, reduce downtime, and improve resource allocation efficiency. Furthermore, the findings demonstrate that increasing repair rates improve MTSF, availability, and overall profitability. It also shows that the state water supply system showing greater sensitivity to repair rate variations. This study contributes a robust analytical framework for optimizing maintenance schedules, minimizing costs, and supporting decision-making. Future work will focus on integrating advanced machine learning techniques and simulation models to further validate and enhance system performance predictions.