How to Scope Your First Predictive Maintenance Pilot

7 min readImplementation

A step-by-step guide to scoping an industrial IoT predictive maintenance pilot—asset selection, signals, connectivity, and success metrics.

Start with one operational pain point

The most successful pilots answer a single question: 'Can we know sooner when this asset class is heading toward failure?' Avoid scoping a 'digital transformation' on day one.

Interview maintenance leads and operators. List the last three unplanned events that hurt production. Pick the asset type that appears most often.

Define measurable success before hardware arrives

Agree on pilot duration (typically 8–12 weeks after data stabilizes), asset count, and what 'success' means—e.g., one avoided failure, 50% reduction in emergency callouts on pilot assets, or alert-to-inspection time under 24 hours.

Choose signals and sampling together

Signal type drives hardware cost and installation complexity. Vibration monitoring on rotating equipment often uses accelerometers with 1–10 kHz sampling for bearing defects. Temperature may need only minute-level logging.

Match connectivity to site layout: wired Ethernet in machine halls, cellular at remote stations, LoRaWAN for distributed level sensors.

Plan installation without stopping production

Magnetic or stud-mounted sensors, non-invasive current clamps, and tap points on existing analog loops reduce downtime during install. Schedule work during planned maintenance windows where possible.

Review weekly during validation

First month is tuning: false positives, missed events, and operator feedback. Weekly reviews with maintenance keep the system grounded in reality. Document every alert and whether it led to useful action.

Tell us what you need to monitor, collect, or control

Whether you need early warning before failure, better process visibility, a custom device, or a cloud platform built around your workflow, we can help define the right scope.

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