Predictive maintenance: where to start with the sensors you already have
Predictive maintenance sounds like a large AI project. In practice the first useful version is usually small: one critical asset, a few signals you may already be collecting, and alerts that reach a technician before a failure. Here is how we approach it.
1. Pick one asset that hurts when it stops
Start where downtime is expensive and failures repeat: a chiller that cools a cold store, a pump station, a compressor or a production-line motor. One asset with a clear cost of failure makes the result easy to judge.
2. Find the data you already have
Many sites already record useful signals without using them:
- Run hours, start counts and alarms in the BMS, SCADA or PLC.
- Energy meters, where rising consumption can show wear.
- Maintenance records and work orders that show when and how the asset failed before.
- Temperature and pressure readings logged for operations or compliance.
Connecting to these sources is often cheaper and faster than installing new hardware, and history from them gives a baseline from day one.
3. Add sensors only for the gaps
If the failures you care about show up as vibration, bearing temperature or current draw, and nothing measures them today, add a few targeted sensors on that asset. Retrofit sensors clamp on without stopping production in most cases.
4. Start with rules, then add models
The first alerts can be simple thresholds and trends that an engineer agrees with: temperature climbing week on week, vibration above the normal band, a motor that starts more often than it should. Machine-learning models come later, once there is enough history to learn what normal looks like for that specific asset.
5. Make the alert reach someone
An alert on a dashboard nobody opens does not prevent a failure. Send it to the technician on duty by SMS, email or WhatsApp, with a clear escalation if nobody responds, and record what was done.
How we run the first step
Our IoT discovery sprint (SAR 20,700, 2 to 3 weeks) surveys the asset, connects to the data you already have and builds a working prototype on it. If you want to know first whether your data can support predictive models, the AI readiness assessment (SAR 13,800, 2 weeks) ranks where AI will pay off and prototypes the top idea. Prices include VAT.