Academic Journal
Analysis of failure data for estimating availability and reliability indicators using statistical and stochastic methods.
| Τίτλος: | Analysis of failure data for estimating availability and reliability indicators using statistical and stochastic methods. |
|---|---|
| Συγγραφείς: | KONWERSKI, Jakub, ZIÓŁKOWSKI, Jarosław, OSZCZYPAŁA, Mateusz, MAŁACHOWSKI, Jerzy, LĘGAS, Aleksandra |
| Πηγή: | Maintenance & Reliability / Eksploatacja i Niezawodność; 2026, Vol. 28 Issue 2, p1-22, 22p |
| Θεματικοί όροι: | Markov processes, Stochastic analysis, Engineering systems, Maintainability (Engineering), Failure analysis, Mathematical statistics, Reliability in engineering |
| Περίληψη: | This paper presents a novel implementation of statistical and stochastic methods for estimating and evaluating reliability and availability indicators in technical systems. Using empirical failure data from a realworld military transport system, we introduce an innovative 7-state model that provides a detailed representation of operational phase of the systems. The research integrates Markov and semi-Markov processes to accurately model state transitions, particularly addressing scenarios where traditional Markov models are insufficient due to non-exponential state distributions. Our findings demonstrate that both statistical and stochastic methods yield closely aligned reliability and availability indicators, validating the robustness of the proposed methodologies. This research not only advances the accuracy of reliability assessments but also identifies actionable improvements to enhance operational readiness. They provide a comprehensive framework for analyzing and improving the operational efficiency of technical systems, with broader applications across various engineering fields. [ABSTRACT FROM AUTHOR] |
| Copyright of Maintenance & Reliability / Eksploatacja i Niezawodność is the property of Polish Scientific & Technical Society Consumables, Polish Maintenance Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
καταχωρήστε σχόλιο πρώτοι!