A Machine Learning–Based Predictive Modeling for Optimizing the

Critical Useful Life of Aeroengines: A Field Maintenance

Perspective

Establishing critical useful life plays a central role to determine aeroengine

health status including aeroengine parameter changes from adverse material

conditions or metal fatigue. The useful life assessment serves to support

maintenance teams by enabling predictive maintenance followed by part

replacement or conditions improvement. The proposed research works to improve

the ability of turbofan aeroengine useful life estimation while targeting

practical deployment during maintenance operations at field locations. A field

maintenance–oriented ensemble bagged regression model for aeroengines represents

the proposed method within this research. The present study reaches an error

index of 7.06 with 98.95% model fitness when applying it to critical useful life

training data. The projected model received its validation through experiments

on test and field datasets. Field tests revealed that among 25 machine learning

models the proposed model delivered optimal results since its error index was

determined at 10.5337 with 97.60% accuracy compared to prior research findings.

This study delivers an optimal solution, which enables aviation maintenance crew

and techno managers to achieve effective critical useful life evaluation and

decision-making for maintenance needs. This research provides essential guidance

to industries under maintenance and repair operations for the reassessment of

field-based critical parameters identification.