Fault Detection and Prognosis of Aerospace Systems Using Long Short-Term Memory Based Recurrent Neural Networks
Bibliographic record
Abstract
Health monitoring and remaining useful life predictions for the aerospace systems is a challenging and complex task to accomplish. Internal or external complications in these aerospace systems (aircraft and satellites) may lead to extremely hazardous or catastrophic consequences to the entire mission involving human life and budget. Considering the severity and complexity of the problem, this thesis deals in developing a diagnosis and prognosis health management system (DPHM) for the attitude actuator control system that uses reaction wheels in pyramid configuration onboard Kepler spacecraft and for the fleet of air-breathing turbofan engines. The established model iscomparatively effective and computationally light in managing the objective of fault detection and prognostics. An advanced data-driven DPHM scheme with optimization techniques is developed and evaluated. Initially, a recurrent LSTM (Long Short-Term Memory) neural network model is established and assessed with the general dataset (Particulate Matter (PM2.5)). Secondly, a statistical-based fault detection method with functional factors of Weibull and mathematical features of frictional parameters showed that reaction wheels 2 and 4 of Kepler spacecraft have an early sign (~2 months) of their respective failures. This statistical method is compared with the proposed LSTM model for validation. Thirdly, the prognostic approach for estimation of remaining useful life (RUL) of the C-MAPPS and PHM08 datasets is successfully achieved. Numerous preprocessing methods such as digital filters (Savitzky-Golay (S-G)), principal component analysis (PCA) are used for standardizing the data. Finally, the optimization tools such as genetic algorithm (GA) and particle swarm optimization (PSO) are merged with LSTM for finetuning the hyper-parameters. Overall, the optimized model performs with better accuracy and can be concluded as a promising algorithm for the health management of complex systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".