Bibliographic record
Abstract
A real-time fault detection and diagnosis technique for linear dynamic control systems is proposed. It provides fault detection and diagnosis using neither observer residuals nor parameter estimation errors, instead, it relies on the estimation of the underlying modal parameters of the dynamic system, and compares the estimates with the pre-calculated characteristic patterns which are represented as a set of root loci of physical parameters. The modal estimation is carried out using a numerically robust least square algorithm based on SVD (Singular Value Decomposition). A pattern recognition technique based on linear multiprototype distance functions is used to classify the faults according to the variation of physical parameters. The method possesses several advantages over the existing techniques: (i) the nature of the fault can be easily identified since the scheme uses physical parameters, rather than model parameters, for classification; (ii) the effect of disturbance on diagnosis is minimized because the modal estimation algorithm treats the disturbance as additional dynamics which are eliminated in the classification stage using truncated SVD; (iii) it is sufficient to use only one measurement signal, since any signal within the control loop contains all necessary modal information for fault diagnosis; and (iv) faults which cause various amount of parameter variation can be easily accommodated by proper selection of parameter ranges in constructing root loci. The method has successfully been applied to a DC servo system.>
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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 | 0.002 |
| 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) | 0.002 | 0.001 |
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".