Year: 2025
Type: article
Abstract: The implementation of predictive maintenance (PM) in aviation presents unique challenges due to strict safety requirements, complex operational environments, and regulatory constraints. This paper develops a comprehensive decision-making framework for evaluating the feasibility of implementing PM for aircraft components, addressing the critical need for systematic integration of technical, economic, and regulatory considerations. Through expert surveys involving 78 aviation maintenance professionals and the application of multi-criteria decision analysis, this study identifies and validates 14 key criteria across four categories: technical and operational, economic and feasibility, regulatory and compliance, and organizational and human factors. The analytic hierarchy process is employed to establish criteria weights, with flight safety impact, reliability predictability, and data sufficiency emerging as primary drivers. The framework’s effectiveness is demonstrated through case studies comparing turbofan engines and avionics units, validating its ability to discriminate between components suitable for PM implementation. Results indicate that successful PM implementation requires not only technological readiness but also organizational alignment and regulatory compliance. This study contributes to aviation maintenance practice by providing a structured, evidence-based approach to PM implementation decisions, while establishing a foundation for future innovations in maintenance strategies. The framework’s practical applicability is enhanced through a detailed implementation roadmap and validation methods, ensuring its relevance for maintenance decision-makers while maintaining alignment with aviation safety standards.
Source: Applied Sciences
DOI: 10.3390/app15031626
Authors: Igor Kabashkin, Roman Fedorov, Vladimir Perekrestov
Institution: Transport and Telecommunication Institute
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4407226730