Remaining Useful Life Prediction of a Turbofan Engine Using Deep Layer Recurrent Neural Networks

It appears your Web browser is not configured to display PDF files. Download adobe Acrobat or click here to download the PDF file.

Click here to download the PDF file.

Creator: 

Thakkar, Unnati Rajeshkumar

Date: 

2021

Abstract: 

Turbofan engine is a pivotal component of the aircraft. Engine components are susceptible to degradation over the life of their operation which affects the performance of an engine. In order to direct the necessary maintenance behavior, remaining useful life prediction is the key. This thesis presents a prediction framework for the Remaining Useful Life (RUL) of an aircraft engine using the whole life cycle data and deterioration parameter data based on a machine learning (ML) approach. In specific, a Deep Layer Recurrent Neural Network (DL-RNN) model is proposed to address the problem of prognostic instability based on deep learning. The proposed method is compared against Multilayer-Perceptron (MLP), Non-linear Auto Regressive Network with Exogenous Inputs (NARX), Cascade Forward Neural Network (CFNN) and validated through the Prognostics and Health Management (PHM) conference Challenge dataset and C-MAPSS dataset provided by NASA results reveal a better predictive precision with respect to other ML algorithms.

Subject: 

Engineering - Electronics and Electrical
Artificial Intelligence

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Applied Science: 
M.App.Sc.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Engineering, Electrical and Computer

Parent Collection: 

Theses and Dissertations

Items in CURVE are protected by copyright, with all rights reserved, unless otherwise indicated. They are made available with permission from the author(s).