Theses and Dissertations
ORCID
https://orcid.org/0000-0001-8640-4273
Issuing Body
Mississippi State University
Advisor
Marufuzzaman, Mohammad
Committee Member
Wang, Haifeng
Committee Member
Tian, Wenmeng
Committee Member
Mittal, Sudip
Committee Member
El Hachmi, Essadiqi
Date of Degree
5-12-2023
Document Type
Dissertation - Open Access
Major
Industrial and systems engineering
Degree Name
Doctor of Philosophy (Ph.D)
College
James Worth Bagley College of Engineering
Department
Department of Industrial and Systems Engineering
Abstract
This dissertation develops systems engineering and machine learning models for aviation maintenance support. With the constant increase in demand for air travel, aviation organizations compete to maintain airworthy aircraft to ensure the safety of passengers. Given the importance of aircraft safety, the aviation sector constantly needs technologies to enhance the maintenance experience, ensure system safety, and limit aircraft downtime. Based on the current literature, the aviation maintenance sector still relies on outdated technologies to maintain aircraft maintenance documentation, including paper-based technical orders. Aviation maintenance documentation contains a mixture of structured and unstructured technical text, mainly inputted by operators, making them prone to error, misunderstanding communication, and inconsistency. This dissertation intends to develop decision support models based on systems engineering and artificial intelligence models that can automate the maintenance documentation system, extract useful information from maintenance work orders, and predict the aircraft's top degrader signals based on textual data. The first chapter of this dissertation introduces the significant setbacks of the aviation industry and provides a working ground for the following chapters. The dissertation's second chapter develops a system engineering framework using model-based systems engineering (MBSE) methodology to model the aviation maintenance process using the systems engineering language (SysML). The outcome of this framework is the design of an automated maintenance system model that can be used to automate maintenance documentation, making it less prone to error. The third chapter of the dissertation uses textual data in maintenance work orders to develop a hybrid approach that uses natural language processing (NLP) and transformer models to predict the readiness of a legacy aircraft. The model was tested using a real-life case study of the EA-6B military aircraft. The fourth chapter of this dissertation develops an ensemble transformer model based on three different transformer models. The ensemble model leverages the benefits of three different transformer architectures and is used to classify events based on an aviation log-based dataset. This dissertation's final and fifth chapter summarizes key findings, proposes future work directions, and provides the dissertation's limitations.
Recommended Citation
Elakramine, Fatine, "Developing systems engineering and machine learning frameworks for the improvement of aviation maintenance" (2023). Theses and Dissertations. 5853.
https://scholarsjunction.msstate.edu/td/5853
Included in
Aviation Safety and Security Commons, Industrial Engineering Commons, Industrial Technology Commons, Maintenance Technology Commons, Other Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Engineering Commons, Systems Engineering and Multidisciplinary Design Optimization Commons