Use Case — Industrial IoT / AI
Industrial IoT & AI Sensor Platform
A multi-plant discrete manufacturer grapples with unplanned spindle failures, coolant chemistry drift, pneumatic leaks invisible to dashboards, supplier quality jitter. leadership mandates a repeatable sensor onboarding kit, anomaly detection that maintenance trusts, and conversational RCA that cites actual historian trends—not hallucinated chatter.
OPC-UA / MQTT hubsMultimodal modelsDigital thread hooksMaintenance copilotPlant cyber modelOEE KPI alignment
Technical layers
- Ingest: High-frequency waveform capture burst mode on anomaly triggers to control storage explosion.
- Features: Spectral fingerprints, cepstrums, pairwise cross-correlations between coupled assets.
- Training discipline: Label faults with SME codes; disallow mixing rebuilt vs worn baseline classes.
- Serving: Shadow deploy candidate models observing live edge before promotion gating KPI delta.
- Humans: Copilot drafts work order text attaching top charts and recommended spare BOM lines—still requires sign-off.
Scale-out pattern
Rolling factory template: onboarding checklist (sensor map, VLAN, PLC tag export), IaC provisioning edge stack, pretrained starter models per asset taxonomy with transfer tweak weeks—not months greenfield modelling per site.