AI-driven Predictive Maintenance for Aerospace Engines

Abstract

This study investigates the innovations, and difficulties, alongside technical application of AI-driven predictive maintenance for aircraft engines. Using a descriptive design and secondary data collection, the study takes a deductive approach and interpretivism as its guiding philosophy. Blockchain, edge computing, adaptive algorithms, in addition to unified communication protocols are all part of the technical framework. Adaptive solutions tackle issues associated with compatibility, data security, and scalability. The field's dynamic nature is revealed by the critical analysis. Subsequent research ought to be focused on improving algorithms, investigating cutting-edge technologies, and handling moral dilemmas.