Remaining Useful Life Prediction of a Turbofan Engine Using Deep Layer Recurrent Neural Networks
Bibliographic record
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 reliability and 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.In addition, for the aircraft engine, a new health indicator (HI) measure is implemented based on the preprocessing of raw data.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 IEEE 2008 Prognostics and Health Management (PHM) conference Challenge dataset and Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset provided by NASA results reveal a better predictive precision with respect to other ML algorithms.I would like to express my deep and sincere gratitude to my research supervisor Dr. Hicham Chaoui for giving me the opportunity to do research and providing invaluable guidance throughout this research.His dynamism, vision, sincerity and motivation have deeply inspired me.It was a great privilege and honor to work and study under his guidance.He is a great teacher, and his doors were always open when I had any question.His prompt replies to the emails even during holidays helped in the rapid progression of this work.I would like to extend my profound gratitude
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".