ABSTRACT Predictive maintenance (PdM) plays a critical role in enhancing safety, operational efficiency and cost‐effectiveness in the aviation industry by enabling condition‐based maintenance strategies instead of traditional schedule‐driven approaches. This paper presents a systematic scoping review of the core technologies underpinning data‐driven PdM in aviation, with a particular focus on digital twin (DT) systems, engineering data management and artificial intelligence (AI) algorithms, and their integration across the PdM pipeline. The reviewed studies are systematically categorised according to three primary data types used in aviation PdM—time‐series sensor data, graphical data and natural language data—together with their associated methods for data collection, preprocessing, storage and analysis. In addition, the review analyses AI‐based approaches for remaining useful life estimation and fault detection, highlighting commonly adopted models and benchmark datasets such as C‐MAPSS. Key challenges identified in the literature include data heterogeneity, real‐time processing constraints, scalability and cybersecurity risks. Emerging solutions, including multimodel database architectures and fog–cloud hybrid computing frameworks, are discussed as enablers of robust and scalable PdM systems. By providing an integrated and aviation‐specific perspective, this review offers researchers and practitioners a structured foundation for the design, development and deployment of DT–enabled PdM systems in safety‐critical aviation environments.
Mehdipour et al. (2026) studied this question.