Advanced Maintenance Strategies for Gas Turbine Engines: A Review of Condition Based, Predictive, and Risk-Based Approaches
Keywords:
Gas Turbine Maintenance, Predictive Maintenance, Condition Based Maintenance, Risk Based Optimization, ReliabilityAbstract
Gas turbine engine maintenance is a critical factor in ensuring reliability, safety, and cost effective operation in aviation and power generation systems. This review examines the development of maintenance strategies from conventional time based maintenance (TBM) to condition based maintenance (CBM), predictive maintenance, and risk based decision frameworks. The review was conducted through a structured analysis of published journal articles, technical reports, and case based studies concerning gas turbine engine performance, maintenance scheduling, and health monitoring applications. The selected literature was evaluated based on maintenance objectives, data requirements, implementation complexity, and reported operational outcomes such as cost efficiency, downtime reduction, and failure prediction capability. The findings indicate that TBM remains useful for standardized maintenance planning but is limited in responding to actual engine condition. In contrast, CBM and predictive maintenance provide improved responsiveness through sensor based monitoring, prognostic health management, and machine learning assisted anomaly detection. Risk based approaches, including Partially Observable Markov Decision Process (POMDP) models, offer stronger support for maintenance optimization under uncertainty by balancing reliability, cost, and operational risk. However, the review also identifies important limitations, including inconsistency in reported performance metrics across studies, high infrastructure requirements, and challenges in integrating digital tools with existing maintenance and regulatory frameworks. Overall, the review highlights that advanced maintenance strategies have strong potential to improve the efficiency and reliability of gas turbine engine operations, particularly when supported by digital monitoring, data analytics, and structured decision making models. Future implementation in aeronautical applications should therefore emphasize scalable data integration, engineering interpretability, and alignment with airworthiness requirements.
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