Dual State and Parameter Estimation for Diagnosis, Prognosis, and Health Monitoring of Electro-Mechanical Actuators
Bibliographic record
Abstract
In this paper, the problem of diagnosis, prognosis, and health monitoring (DPHM) for electro-mechanical actuators (EMA) under degradation and fault is studied. The performance of the EMA is crucial in flight control systems (FCS), and hence, degradation in these systems should be accurately monitored. A dual state and parameter estimation methodology by utilizing particle filters (PF) is used in this work to estimate the states of EMA and their degradations. Moreover, the PF have been used to predict the evolution of states and degradation in the EMA, which can be used to estimate and evaluate their remaining useful life (RUL). In the provided numerical case study, the effectiveness of the dual state and parameter method in estimating and predicting states and degradation for a given electro-mechanical actuator is provided and the RUL of the system is predicted. Moreover, the predicted probability of failure by utilizing the estimated states and parameters up to the t-th instant as an indicator of the accuracy of our DPHM methodology is obtained.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".