Neural Parameter Estimators for hybrid fault diagnosis and estimation in nonlinear systems
Bibliographic record
Abstract
This paper presents a novel hybrid fault diagnosis approach to detect and estimate component faults in general nonlinear systems with full-state measurement. Unlike most existing fault diagnosis techniques, the proposed solution provides an integrated framework to simultaneously detect, isolate, and estimate the severity of faults in system components. The proposed solution consists of a bank of adaptive Neural Parameter Estimators (NPE) where each NPE in the bank is designed based on a separate parameterized fault model. Each NPE in the bank estimates its corresponding unknown Fault Parameter (FP) that is further used for fault detection and estimation purposes. Fast convergence and simple isolation policy are among the characteristic features of our proposed solution. Static neural network architecture and simple weight adaptation laws also make the proposed technique appropriate for real-time implementations. Simulation results reveal the effectiveness of the developed scheme in detecting, isolating and estimating faults in components of reaction wheel actuators of a 3-axis stabilized satellite even in presence of satellite disturbances.
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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".