Predictive Maintenance for Component Test Cells
Challenge
A global heavy-equipment manufacturer runs instrumented test benches — roughly 70 sensors per cell — to validate a critical driveline component at one of its plants. When a test deviates, the expensive question is whether the bench infrastructure or the component under test is at fault — and unplanned bench downtime stalls everything scheduled behind it.
The Engagement
Brainiall was contracted to build a predictive-maintenance model and a monitoring dashboard for the test cells, on a May–September 2026 schedule: from field mapping through model training, dashboard deployment and live validation — with scheduled retraining as operating data accumulates.
Delivered so far
- On-site mapping of the bench and its data sources
- Consolidation and treatment of the full telemetry history
- Statistical analysis and selection of the model variables
- Training-set engineering — premises ratified with the client's maintenance leadership in a joint technical checkpoint
Model training is underway, on schedule.
The approach
Rather than a single global alarm threshold, the models learn per-variant behavior baselines directly from telemetry — the cells test many distinct component models — and score deviations against the right baseline, helping separate bench-infrastructure signatures from component behavior.
Status
Nearly complete: final results pending data from the client (September 2026).