Enhancing structural anomaly detection using a bounded autoregressive component
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
Structural Health Monitoring has the potential to enhance the safety and serviceability of our aging infrastructures by detecting anomalies at an early stage. Bayesian Dynamic Linear Models (BDLM) have been shown to be effective at detecting anomalies by extracting structural patterns and latent variables from complex and noisy time series. However, the autoregressive component modelling the stationary prediction errors in most BDLM has a tendency to wrongfully capture patterns that should be attributed to anomalies, and thus hinders their detectability. This paper proposes a new bounded autoregressive (BAR) component, which imposes constraints on the autoregressive latent process with a new mixture Rectified Linear activation Unit. The BAR component is probabilistically verified on synthetic data using a new F1t metric, and is validated using real observations collected on a bridge and on a dam located in Canada. The experimental results demonstrate that the BAR model surpasses the performance of the existing autoregressive component with (1) an improved accuracy at estimating hidden states, (2) an early detection of anomalies, (3) a capacity to detect smaller anomaly magnitudes, and (4) the ability to control the tradeoff between the anomaly detectability and the false alarm rate.
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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 it