Anomaly Detection Method of Aircraft System using Multivariate Time Series Clustering and Classification Techniques
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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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".