The Boeing 787 Dreamliner is powered by the GEnx-1B turbofan engine, a highly efficient propulsion system that relies on advanced digital control and diagnostic technologies to ensure safe and reliable operation. Traditional maintenance strategies based on reactive or scheduled interventions are increasingly being replaced by predictive and condition-based approaches. This study proposes an integrated analytical framework for sustainable fault detection and predictive maintenance of the GEnx-1B engine, combining the Full Authority Digital Engine Control (FADEC), Engine Electronic Controller (EEC), Engine Monitoring Unit (EMU), and Common Data Network (CDN). The paper analyzes the data acquisition and communication architecture of these subsystems and examines how condition-based maintenance (CBM) and reliability-centered maintenance (RCM) principles can be enhanced using machine learning approaches such as multilayer perceptrons (MLP), long short-term memory (LSTM) networks, and adaptive neuro-fuzzy inference systems (ANFIS). A literature-grounded analytical comparison of diagnostic workflows and predictive maintenance strategies illustrates the expected benefits of integrating onboard systems with AI-driven analytics, including earlier anomaly detection and improved maintenance planning. The proposed framework supports the transition from reactive to predictive maintenance in modern aviation and provides a conceptual foundation for future data-validated implementations on next-generation aircraft engines.
Keywords
The GEnx-1BFADECEECEMUCDNEdge AIFault DiagnosisMaintenanceCBMRCMPredictive MaintenanceAIEngine MonitoringBoeing 787Flight DataACMS
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