Real-Time Satellite Telemetry Anomaly Detection and Triage Workflow

Manual monitoring of telemetry dashboards is reactive and prone to oversight. A custom workflow ingests streams from S-band and space-to-ground links, applies statistical baselining and ML models (Isolation Forest, LSTM) in near real-time, and surfaces anomalies within seconds. This shifts operations from periodic checks to continuous surveillance, catching subsystem degradation or fault signatures before they escalate into outages.

Raw threshold-based alerts overwhelm engineering teams with noise. An intelligent triage layer classifies anomalies by subsystem (e.g., power, thermal, AOCS), correlates them with known space weather events or maneuver schedules, and assigns a severity score. Only high-confidence, actionable alerts are routed, suppressing thousands of non-critical notifications per week and allowing engineers to focus on genuine threats.

Manual alert routing delays response. The workflow uses a rules-and-agent-based router that maps anomaly signatures to responsible teams (Power, GNC, Payload). It automatically creates enriched tickets in ServiceNow or Jira with relevant telemetry snapshots, historical context from a vector database, and suggested diagnostic steps, ensuring the right engineer gets the complete context in under a minute.

The workflow automates the repetitive, high-cognitive-load tasks of monitoring, initial diagnosis, and ticket logging typically handled by Level 1/2 operations engineers. This reallocates ~2 FTE per shift from watchstanding to higher-value engineering analysis, failure investigation, and fleet optimization, directly reducing headcount needs as the constellation scales from dozens to hundreds of satellites.

Unaddressed anomalies can cascade. The architecture includes automated containment actions: upon detecting a critical fault pattern (e.g., memory SEU, reaction wheel saturation), it can trigger a pre-validated safe-mode transition script, isolating the subsystem and preserving the asset while humans are notified. This built-in response layer reduces the window of vulnerability and potential for total satellite loss.

The workflow logs every anomaly detection, scoring decision, routing action, and automated response with timestamps and model confidence scores. This immutable audit trail, stored in a time-series database, provides defensible evidence of due diligence in satellite operations for regulators (FCC, ITU) and insurers, potentially lowering liability premiums and simplifying licensing renewals.

This foundational component ingests high-frequency telemetry streams (S-band, X-band) from satellites and ground stations via protocols like CCSDS, Kafka, or MQTT. It normalizes disparate data formats (e.g., TDM, Space Packet) into a unified time-series schema, handling data gaps and jitter. Without this automated pipeline, engineers waste cycles manually stitching together data from siloed subsystems.

A dedicated AI agent applies a hybrid model stack in real time: statistical process control (CUSUM, EWMA) for known parameter limits and unsupervised ML models (Isolation Forest, Autoencoders) for novel fault detection. It outputs anomaly scores and confidence intervals, triggering alerts only when thresholds are breached. This replaces manual limit-checking spreadsheets and reduces false positives by 60-80%.

The core orchestrator (built with LangGraph or Temporal) classifies anomalies by subsystem (e.g., Power, ADCS, Thermal), severity, and satellite. It routes alerts using business rules: critical power faults go directly to the on-call engineer via PagerDuty, while minor thermal drifts create a Jira ticket for the thermal team's backlog. This eliminates broadcast alerting and ensures the right engineer sees the right issue first.

For medium-confidence anomalies, the workflow automatically generates a diagnostic summary and routes it to a human review queue in a dashboard like Grafana or ServiceNow. Engineers can confirm, dismiss, or escalate. All actions are logged for audit. This control layer prevents fully autonomous responses to ambiguous signals, maintaining safety and governance required for high-value assets.

A dedicated monitoring agent tracks the health of the workflow itself: data pipeline latency, model drift (via PSI), alert volume trends, and triage accuracy. It feeds metrics to Prometheus and triggers retraining jobs or pipeline repairs. This operationalizes the ML pipeline, ensuring detection performance doesn't degrade as the constellation scales or satellite behavior ages.

The workflow is not a silo. It integrates via REST APIs and webhooks with: ServiceNow/Jira for ticketing, Slack/Teams for notifications, Splunk/Datadog for log correlation, and mission control software (e.g., AGI STK, FreeFlyer) for contextual orbital data. This architecture turns alerts into actionable work items within existing operator workflows, avoiding yet another dashboard.