Affiliation:
1. University of North Texas, USA
Abstract
This book chapter offers an examination of the transformative influence of Artificial Intelligence (AI) within the aviation sector, focusing specifically on its application in predictive maintenance for enhancing operational efficiency. Through the investigation of current research and industry trends, and the utilization of a literature review as its methodological framework, this chapter elucidates the transformative impact of AI-driven predictive maintenance strategies on aviation operations. It explores how AI algorithms analyze vast amounts of data to predict potential equipment failures, enabling proactive maintenance interventions that minimize downtime and optimize fleet performance, presenting an analysis of the implications of AI integration in aviation. Notably, the integration of AI-driven technologies in critical areas such as flight planning, predictive maintenance, and air traffic management is highlighted, showcasing the significant advancements that have reshaped the aviation landscape. Furthermore, the chapter adopts a user-centric perspective, offering a critical assessment of the challenges and considerations inherent in AI implementation in aviation. This includes examining issues pertaining to human-machine interaction, trust in AI systems, and the evolving dynamics of job roles within the industry. Overall, the chapter provides a comprehensive overview of AI's impact on aviation, offering valuable insights for aviation professionals, policymakers, and researchers seeking a deeper understanding of the profound changes driven by AI within the aviation domain.
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