Early Warning System for Engine Failure Detection in Aircraft Engines Using Machine Learning
- Title
- Early Warning System for Engine Failure Detection in Aircraft Engines Using Machine Learning
- Creator
- Biju, Albin; Prasad, Smrity; Kalaivani, S.
- Description
- Aviation has a problem with engine defects which are a major concern. Unforeseen causes might render them expensive on the ground and hazardous in the air. We present a system that signals when an aircraft engine is about to fail. Our AdvancedModelTrainer checks a collection of models - Random Forest, XGBoost, Gradient Boosting, LightGBM, Ridge, Lasso, ElasticNet, and a simple neural network - through a dataset of 10,000 engine cycles along with 25 engineered features. Hyperparameter tuning and Remaining Useful Life (RUL) metrics help to select the top two (Gradient Boosting and XGBoost, RMSE 39.99, R2=0.7715). A complete MLOps structure keeps an eye on the drift, initiates the retraining process, and sets up dashboards that are user-friendly for the mechanics. The system has detected on 1,433 new engines, 1,126 were classified as Safe, 106 as Warning, and 201 as Critical, which is indicating the coverage of 93.44The dataset used was completely anonymized in order to safeguard sensitive operational data and to not conflict with the aviation data privacy regulations. 2025 IEEE.
- Source
- Proceedings of the IEEE International Conference Image Information Processing;pp.483-489
- Date
- 01-01-2025
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- aircraft engines; early warning; machine learning; MLOps; predictive maintenance; remaining useful life
- Coverage
- Biju A., Christ University, Bangalore, India; Prasad S., Christ University, Bangalore, India; Kalaivani S., Christ University, Bangalore, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISSN: 2640074X; ISBN: 979-833155618-1;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Biju, Albin; Prasad, Smrity; Kalaivani, S., “Early Warning System for Engine Failure Detection in Aircraft Engines Using Machine Learning,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 21, 2026, https://archives.christuniversity.in/items/show/26034.