Anomaly Detection
Find what's wrong before anyone reports it — including what no rule covers.
Most monitoring tools detect anomalies you already know to look for — by threshold, by rule. PULSAR goes further. By learning normal behavior from the Dynamic Twin, it detects three classes of anomalies: known failure patterns, rare statistical outliers, and behavioral drift that would only become visible in a late integration cycle — or in the field. The earlier a problem surfaces, the cheaper it is to fix.
Visual: anomaly learning over time (e.g. SDVDiag-style figure) or per-domain health traffic lights.
Key aspects
Detection scope diagram placeholder.
- AI-driven model selection: picks the right anomaly model per signal type.
- Improves over time: false positives shrink as more data is observed.
- System-wide: across the full dependency graph — not per-ECU silos.
- Three classes: known patterns · rare outliers · invisible drift.
Outcomes & KPIs
Detection scope
Known + hidden drift
Beyond thresholds — behavioral signals tied to the twin.
Integration cycles
Earlier discovery
Surface issues before SOP, not only after.
False positives
↓ over time
Continuous improvement with each run.
Target groups: Test & Validation · Heads of Quality · Warranty Managers
Use cases by industry
How teams apply this capability in their sector (more industries coming soon).