Fault Diagnosis of Nonlinear Systems using a Hybrid-Degree Dual Cubature-based Estimation Scheme
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper a novel hybrid-degree dual cubature-based nonlinear filtering methodology is proposed for fault diagnosis of nonlinear systems subject to multiplicative component faults. Distinct from conventional dual estimation schemes, the nonlinear functions are approximated with cubature rules to achieve a designated and case-dependent degree of accuracy. Our methodology is motivated from two primary observations: (i) dynamic characteristics of system states and parameters generally are distinct and posses different degrees of complexities, and (ii) performance of cubature rules depend on the system dynamics and vary when approximate high-dimensional integrations are utilized. The boundedness of the estimation error covariance and stability analysis are formally investigated in presence of approximation errors due to cubature rules, uncertainties, and noise. The effectiveness of our proposed methodology is evaluated by application to a gas turbine engine for addressing the multi-mode component fault diagnosis problem within an integrated fault detection, isolation and identification framework. Case studies are provided to substantiate the superiority of the proposed methodology when compared with those of other representative filters including the Unscented Kalman Filters (UKF) and Particle Filters (PF).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it