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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
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The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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Optimisation of Maintenance Policies Based on Right-Censored Failure Data Using a Semi-Markovian Approach.

Antonio Sánchez-Herguedas1, Angel Mena-Nieto2, Francisco Rodrigo-Muñoz3

  • 1Department of Industrial Management, School of Engineering, University of Seville, Camino de los Descubrimientos s/n, 41092 Seville, Spain.

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Summary

Optimizing industrial preventive maintenance intervals is crucial, especially with right-censored data. This study presents a z-transform and semi-Markovian method to improve maintenance scheduling and achieve economic benefits.

Keywords:
finite horizonmaintenance costmaintenance intervalmaintenance modelright-censored datasemi-Markov process

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Area of Science:

  • Industrial Engineering
  • Reliability Engineering
  • Operations Research

Background:

  • Optimal industrial preventive maintenance (IPM) interval determination is challenged by right-censored data from sensor networks.
  • Right-censored data, where the exact failure time is unknown, complicates accurate maintenance planning.
  • Existing methods may not provide consistent mathematical solutions for IPM intervals with such data.

Purpose of the Study:

  • To address the challenges of determining optimal IPM intervals using right-censored failure data.
  • To introduce a novel methodology combining z-transform and semi-Markovian approaches for more consistent IPM solutions.
  • To demonstrate the practical application and economic benefits of the proposed methodology in a real-world case study.

Main Methods:

  • Development of a methodology integrating z-transform and semi-Markovian processes.
  • Application of the methodology to a case study involving the maintenance of large marine engines.
  • Analysis of the impact of right-censored failure data on optimal preventive interval calculations.

Main Results:

  • The use of right-censored failure data significantly reduces the calculated optimal preventive maintenance interval.
  • Older failure data, when considered, lead to an increase in the optimal preventive interval.
  • The proposed methodology offers a more consistent mathematical solution for IPM interval optimization.

Conclusions:

  • The proposed z-transform and semi-Markovian methodology effectively optimizes industrial preventive maintenance intervals with right-censored data.
  • Maintenance managers can achieve noticeable economic improvements by adjusting preventive intervals using this approach.
  • The methodology's relevance is confirmed for data with at least 75% duration of the preventive interval.