An Ensembled Regression based Remaining Useful Life Prediction of Aero Engines: A Field Maintenance Perspective

In the Aircraft Maintenance Engineering and Management field, the Remaining Useful Life (RUL) estimation has a prime role to determine the health condition of aeroengines. The RUL assessment further assist the maintenance team in ensuring predictive maintenance and enhancing the operational capability of the fleet. In recent, computational based approaches have had a high rise in predicting useful life through the machine learning-based predictive modelling based on the aero engine simulation and associated performance parameters. However, there is a significant difference during implementation at field level. In this paper, an Ensembled Bagged Regression-based machine learning model of aeroengines tailored with field maintenance perspective is proposed. The proposed study achieves high precision and accuracy while assessing RUL as compared with previous researches. The projected model is validated on test and field datasets wherein the model fitness is found to be very optimised. Additionally, the efficacy of the proposed model tested against 25 different machine learning models in terms of performance and fitness. This paper is very useful to provide the optimal solution to the maintenance team, and techno managers for ensuring effective RUL assessment and decision making in aviation maintenance. This paper is also valuable for Maintenance Repair and Operations (MRO) based industries to identify and reassess the critical parameters analysed through filed viewpoint. Supplementary Material File (rul_shaktiyavesh_19mar25.docx) - Download - 487.30 KB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 434views 74downloads Citations Download citation Shaktiyavesh Nandan Pratap Singh, Manish Chaturvedi, Rohitashwa Shringi, et al. An Ensembled Regression based Remaining Useful Life Prediction of Aero Engines: A Field Maintenance Perspective. Authorea. 20 March 2025. DOI: https://doi.org/10.22541/au.174250095.54366773/v1 DOI: https://doi.org/10.22541/au.174250095.54366773/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.