Aero Engine Remaining Useful Life Prediction and Defect Detection using Deep learning and Machine Learning

Anthony, Akhil (2024) Aero Engine Remaining Useful Life Prediction and Defect Detection using Deep learning and Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

In the rapidly evolving aviation industry, ensuring safety and efficiency of aircraft’s is essential. This research investigates whether the integration of image processing algorithms and predictive maintenance algorithms can enhance the efficiency and reliability of aircraft engine maintenance operations. This paper addresses vulnerabilities in the traditional maintenance practices by focusing on real-life incidents which highlight the need for better maintenance systems which can be used during manufacturing and during the flights. This paper proposes a comprehensive system using hybrid models CNN-BiLSTM, which is a combination of deep learning models for RUL prediction and a hybrid model VGG16-SVM, which is a combination of deep learning and machine learning model for defect detection. The evaluation of the models showed CNN-BiLSTM model obtained an MAE of 48.03 and the VGGSVM model achieved an overall accuracy of 90.79% The developed web application, designed for maintenance personnel demonstrates the systems ability to transform the aerospace maintenance. The findings indicate that integration of advanced image processing and predictive maintenance algorithms can improve the safety and efficiency of the aircraft engines. Thus the study proves, the combination of image processing and predictive maintenance techniques can enhance the aerospace maintenance operations.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Rifai, Hicham UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Aviation Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	07 Aug 2025 08:44
Last Modified:	07 Aug 2025 08:44
URI:	https://norma.ncirl.ie/id/eprint/8455

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