Particle Filter-Based Prognosis and Health Monitoring of Electromechanical Actuators
Bibliographic record
Abstract
Given the important role of Electromechanical Actuators (EMAs) in the aviation industry, this paper aims to develop Prognosis and Health Monitoring (PHM) solutions for EMAs. We begin by analyzing the general configuration and architecture of EMAs and demonstrate that load torque oscillation induces amplitude modulation in the stator current. We also propose a relationship between two faults, namely spiral bevel gear and flex spline wear. Next, we model an EMA, including a brushless DC motor, inverter, gearbox, mechanical load, and other units. As a prerequisite for fault prediction, we address the estimation of two states: stator current and motor speed. We use a Particle Filter-based (PF) methodology to estimate these states and perform predictions. The prediction scheme involves forming an auxiliary state corresponding to fault degradation, based on which the remaining useful life (RUL) of the system is computed. Finally, we present extensive simulation results of the proposed methodology corresponding to various scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".