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The functioning of any system or product for a long period is very essential. To ensure a system or product remains functional for a desired time period, reliability is the crucial characteristic that must be prioritized. The main objective of this paper is to analyse the reliability measures like as reliability, availability, Mean Time to Failure (MTTF) of Drip irrigation system by employing the supplementary variable technique (SVT) and Markov process to formulate the proposed model. Then, we assess the complex system’s state probabilities, up and down state probabilities by Laplace transforms. The failure and repair rate for this system is presumed to be general. The best reliability of the complex system is attained using a neural network algorithm. A graphical representation of the results is also provided to better understanding of the study.

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Reliability Analysis of Complex System Using Markov Process and Neural Network Algorithm

  • 会议论文

  • 发表日期：

    2026年

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  • Deepanshi,
  • Nupur Goyal,
  • Akansha Gupta

摘要

The functioning of any system or product for a long period is very essential. To ensure a system or product remains functional for a desired time period, reliability is the crucial characteristic that must be prioritized. The main objective of this paper is to analyse the reliability measures like as reliability, availability, Mean Time to Failure (MTTF) of Drip irrigation system by employing the supplementary variable technique (SVT) and Markov process to formulate the proposed model. Then, we assess the complex system’s state probabilities, up and down state probabilities by Laplace transforms. The failure and repair rate for this system is presumed to be general. The best reliability of the complex system is attained using a neural network algorithm. A graphical representation of the results is also provided to better understanding of the study.