Anomaly detection with switching Kalman filter and imitation learning
Bibliographic record
Abstract
The detection of anomalies in structural health monitoring (SHM) time-series is notoriously difficult, because one needs to separate reversible patterns caused by the environmental factors and loading, from the baseline irreversible degradation, whereas anomalies typically consist in long-term drifts that have an effect that is orders of magnitude smaller than the reversible patterns. The switching Kalman filter (SKF) has been shown to be an effective tool to address these challenges by quantifying the probability of a switch between different regimes corresponding to normal stationary conditions and abnormal non-stationary ones. Current applications of the SKF have relied on the probability of regime switch as a proxy for detecting anomalies. Despite its capacity to outperform threshold-based detection approaches, it remains prone to (1) missed alarms when the reversible and irreversible pattern separation fails, and (2) false alarms when the predictive capacity of the stationary model is too limited. Previous works have shown that these issues can be mitigated by replacing an alarm triggering policy based solely on the probability of regime switch, with imitation learning (IL) agents that build policies based on the probability of regime switch as well as the trend hidden state. This study further enhances the IL agents by including high-dimensional hidden states vectors as well as by leveraging multiple time steps from the data history. The IL agents that have learnt the underlying dependencies between the high-dimensional hidden state estimates and the labeled actions, which in our context, are whether to trigger an alarm or not, are tested on time series including synthetic and real ones collected on a bridge located in Canada. The results show that the new method provides anomaly detection agents that improve the detectability of anomalies, and are self-adaptive to new stationary conditions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".