APPLICATION OF MARKOV CHAINS, MTBF AND MACHINE LEARNING IN AIR TRANSPORT RELIABILITY
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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 provide insights into 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 importance in shaping the future of maintenance practices in the air transportation industry.
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