Cloud-scale industrial intelligence that detects machine faults before they happen, identifies root cause automatically, and retrains its own AI models — without human intervention.
Traditional monitoring systems trigger alerts only after a fault becomes obvious. By then, the damage is done and the downtime is inevitable. DIANA detects the early signatures of failure minutes — or hours — before a human would notice anything unusual.
DIANA operates as a unified intelligence stack — perception at the edge, coordination at the gateway, cognition in the cloud.
Lightweight AI models run directly on each machine, continuously analyzing vibration, temperature, and RPM in real time. When the model's confidence in normal operation drops, it escalates immediately — not when a threshold is crossed.
A stream processing layer correlates signals across the entire machine fleet, determining whether a fault is isolated to one unit or symptomatic of a wider systemic issue. Device state is continuously synchronized across every node.
A causal reasoning engine receives escalations, determines root cause — bearing fatigue, overheating, rotor imbalance, electrical fault — and estimates time to failure. It then triggers a federated learning round and redeploys improved models to the fleet within minutes.
The system gets smarter with every anomaly detected. When a fault is identified, edge models are retrained on real fault data from the entire fleet and redeployed automatically — no engineer required.
No raw sensor data ever leaves the machines. Only model weight updates are shared during learning rounds, preserving operational data privacy and dramatically reducing bandwidth requirements across the fleet.
New models are pushed over the air and hot-swapped on running devices without interruption. The machine never stops. Inference continues unbroken during the model transition window.
Every escalation generates a plain-English operator explanation: what happened, why it happened, and what to do about it. Operators act on insight — not just numbers on a screen.
Operators see every machine's live health score, confidence rating, and active fault diagnosis in a single view. When something goes wrong, they know what it is, why it happened, and how long they have.
DIANA's federated learning cycle means the entire fleet benefits from what any single machine learns — while no raw data ever leaves the device.
Edge model confidence drops. Anomaly signature is captured locally on the device.
Gateway relays the escalation. Cognition layer performs causal diagnosis and root cause classification.
Each machine trains locally on its own fault data and contributes encrypted weight updates — never raw data.
The cloud aggregates fleet-wide updates into an improved global model capturing new fault signatures.
New model is pushed over the air and hot-swapped on all edge devices. Zero downtime. Fleet-wide improvement within minutes.
DIANA gives industrial operators complete situational awareness — before a problem surfaces, before a threshold is crossed, before a human would notice anything unusual.