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

Client name withheld under NDA. Status as of the June 2026 joint checkpoint: on schedule, with no scope deviations.