Adaptive Kalman Filter by Reinforcement Learning for Monitoring Aircraft Engines' Performance Against Abrupt Events
Bibliographic record
Abstract
Abstract Engine performance's inverse problem is a well-known subject in the context of engine monitoring, particularly important for the aeronautics industry. In this framework, we aim to construct health/performance indicators (such as modular efficiencies and air mass flow rates) by leveraging operational data (i.e., sensors' measurements during flights) through the availability of a forward model (e.g., a thermodynamic simulator). An extensive literature is available on this topic, among which, Bayesian filtering—notably, Kalman filtering—is a dominant approach. However, even state-of-the-art methods still underperform in a scenario often found in practice: during its life, engine components not only degrade gradually over time due to wear but also can experience rare, abrupt changes in health states caused by uninformed maintenance or unknown external events such as Foreign Object Damages. In this work, we focus on this challenging scenario. We observe that Kalman filters (KF), when equipped with well-tuned a priori models, are capable of estimating the evolution of performance indicators due to degradations, but fail (if using the same a priori models) whenever an abrupt event occurs. To address this, we propose an adaptive filtering method, where parameters of the associated models are dynamically adjusted based on current estimates and observations. In particular, we propose a reinforcement learning (RL) agent, called single-filter reinforcement learning Kalman filter (RLKF), to control the noise covariance matrix of the transition function of a Kalman filter. Pushing one step further, we introduce a second agent, called double-filter RLKF, aided by launching alongside a nonadaptive filter predicting the moments of abrupt events. We conduct several experiments with simulated data of an in-house turbofan engine, and show the superiority of the adaptive filters with the proposed reinforcement learning agents.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".