Anomaly Detection Method of Aircraft System using Multivariate Time Series Clustering and Classification Techniques
Why this work is in the frame
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Bibliographic record
Abstract
The paper presents an anomaly detection method that identifies and explains anomalies in an aircraft system based on explainable multivariate time series clustering techniques. The method considers the cyclicity of each variable within the flight phases and selects those behind the anomalies. It combines the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and a modified Dynamic Time Warping (DTW) distance algorithms to detect abnormal behavioral profiles within the collected flight phases without any prior knowledge on the system's behavior. The proposed method explains those abnormal profiles compared to normal profiles using a new importance score. Profiles are detected using the Time Series Forest (TSF) and the silhouette criterion. The method is trained and tested using a sample from the Bombardier's Aircraft Health Monitoring System. It distinguishes the normal and abnormal behaviors by achieving a clustering silhouette score of 0.95 and detects unknown profiles with a precision of 89%.
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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 | 0.001 | 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