The model combines feature windows, sequence embeddings, and threshold calibration for low false-positive rates.
windowed = telemetry.rolling(window=24).mean().dropna()
alerts = model.predict(windowed) > 0.78
P(fault|x) = sigma(Wx + b)
Aug 16, 2026 · 1 min read
A temporal anomaly detection workflow for satellite health telemetry and predictive maintenance alerts.
The model combines feature windows, sequence embeddings, and threshold calibration for low false-positive rates.
windowed = telemetry.rolling(window=24).mean().dropna()
alerts = model.predict(windowed) > 0.78
P(fault|x) = sigma(Wx + b)

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