Project case study · 2026
PX4 Flight Log Anomaly Analyzer
An interpretable anomaly-detection system for PX4 flight logs that combines unsupervised ML scoring with rule-based findings.
Problem
PX4 logs contain dense, multivariate signals that make abnormal behavior difficult to spot manually.
Approach
The system extracts 32 raw and 23 engineered features, trains on 110 normal multirotor logs, compares five unsupervised approaches, and combines model scores with interpretable operational rules.
Architecture
.ulg upload → parser → 55-feature pipeline → OCSVM scoring + rule detectors → contribution analysis → Streamlit dashboard
Evaluation
OCSVM with an RBF kernel reached 0.940 AUC on a held-out set of 34 normal and 31 anomalous logs, ahead of the four reported alternatives.
Product
The Streamlit interface brings together summary metrics, a score timeline, anomaly contributors, rule overlays, and a signal explorer so an operator can move from a flagged interval to the telemetry behind it.
Limitations
Training is multirotor-only. The evaluation set contains 65 logs, GPS-only fault sensitivity is limited, and thresholds are relative within each log rather than globally calibrated.