Getting started with predictive maintenance

You don't need a data science team to begin — start with your most critical assets and a single failure mode.

Predictive maintenance has a reputation problem: it sounds like a multi-year digital-transformation programme. In practice, the teams that succeed start embarrassingly small — one asset class, one failure mode, one sensor type — and expand only when the first win is proven.

Rank your equipment by what a failure actually costs: lost production, safety risk, repair lead time. Monitor the top of that list, not the assets that happen to be easy to reach.

Step 1 — pick the assets that hurt

Bearings announce themselves through vibration and temperature. Electrical faults show up in current draw. Leaks sing in ultrasonic. Choose the measurement that matches the failure you're trying to catch — not the sensor with the flashiest spec sheet.

Step 2 — pick the failure mode, then the signal

An alert that doesn't create work is just noise. Decide upfront who sees a warning, what a critical alert triggers, and how the finding lands on a work order. The monitoring technology is the easy part; the response loop is where value is created.

Step 3 — wire it to a workflow

Track the catches — the failures your monitoring surfaced early. That evidence is what earns the budget to expand coverage from five assets to fifty.

Step 4 — prove it, then scale