This pattern predicts or detects degradation using contextual telemetry and optional vision. It prioritises actionable lead time and controlled deployment over an isolated offline accuracy score.
Data contract
Define asset, channel, unit, sampling, clock, operating state and quality flags. Join maintenance actions and confirmed failure modes using event time. Use store-and-forward gateways and preserve raw windows needed for root-cause review. Exclude shutdowns and recipe changes from “failure” labels unless intended.
Platform roles
AxiOverseer runs live feature extraction, multi-stream vision and retraining/validation separation across two GPUs. AxiRelay serves multiple independent models. AxiCrucible separates production service onto one node and training/maintenance onto another with four 96GB GPUs.
Model development
Start with thresholds and statistical baselines. Split training and test data by time and asset to prevent leakage. Evaluate precision/recall at the event level, warning lead time and alarm burden. Use vision only where it adds evidence and document lighting/camera conditions.
Deployment
Register the feature code, model, threshold and intended asset population. Run shadow mode, then issue advisory alerts to named roles. Integrate with the maintenance workflow so disposition returns as learning data. Do not automatically stop or control equipment without a separately validated safety/control design.
Monitoring
Prometheus can monitor service health while domain metrics track missing channels, drift, warning lead time, false alerts and missed events. MLflow can track experiments and packaged models. Retain a rules-based fallback and a clear method to suppress a faulty release.
Primary technical references
- NVIDIA DeepStream documentation
- MLflow Tracking documentation
- MLflow model deployment documentation
- Prometheus overview
References are provided for software architecture and implementation planning. Validate the versions, licences, support matrix and regulated-use requirements applicable to the final deployment.
From technical concept to production system
Apply this technology through an Axiotech engineering work package
Axiotech can connect the compute, AI or analytics platform to the machine controls, data contracts, validation evidence and lifecycle-support model required for industrial use.
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