MIMO
Multiple-input, multiple-output residual tracking across an entire subsystem.
Edge-native digital-twin AI for condition-based maintenance — unsupervised anomaly detection, explainable diagnostics, and bandwidth-safe compression for critical assets operating in DDIL environments.
StateBooks fuses multivariate sensor measurements into a single signal, then clusters normal operating behavior into a compact set of reference “state vectors” — a StateBook. Growing or sustained residuals indicate degradation long before a threshold alarm would trip.

Multiple-input, multiple-output residual tracking across an entire subsystem.
Zero labeled failure data required; works on any correlated asset or system.
Predictions continue through sensor dropout.
Predicts unmeasured parameters and tracks changes via system identification.
Adapts to sensor drift, aging equipment, and mission-context shifts.
50–200x sensor-stream reduction before any shore/off-platform transmission.
Model weights, not raw data, synchronize across a fleet.
New subsystems instantiated via configuration file, no source-code changes.
Edge runtime paired with a cloud backend on AWS GovCloud. Architecture allows for 1-way communication, and a documented API integrates with Navy CMAS / 2Kilo workflows. Federated learning keeps raw data on-platform fleet-wide.
Figures reflect State Signal AI internal testing and Phase II target thresholds for the Navy CBM+ program.