APPLICATION OF MARKOV CHAINS, MTBF AND MACHINE LEARNING IN AIR TRANSPORT RELIABILITY

Main Article Content

Justyna Tomaszewska
https://orcid.org/0000-0001-6883-7235

Abstract

Air transport reliability is a critical aspect in enhancing passenger satisfaction, network connectivity, safety, environmental sustainability, and operational efficiency. In the air transport industry, the reliability of critical components and systems plays an important role in ensuring the safety and efficiency of air transport systems. This article explores the integration of advanced methodologies, including Markov chains, mean time between failure (MTBF) analysis and machine learning, as promising ways to improve the reliability. In addition, this article provides an overview of in-service data provides, insights into future prospects and discussions of challenges, regulatory implications, and industry collaboration further contribute to a comprehensive understanding of the application of machine learning and MTBF analysis in air transport reliability. The diverse applications and evolving trends in predictive maintenance underscore its significance in shaping the future of maintenance practices in the air transportation industry.

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How to Cite
Tomaszewska, J. (2023). APPLICATION OF MARKOV CHAINS, MTBF AND MACHINE LEARNING IN AIR TRANSPORT RELIABILITY. Aviation and Security Issues, 4(2). https://doi.org/10.55676/asi.v4i2.81
Section
Articles

References

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