Predictive Maintenance
Predictive maintenance uses condition data and historical trends to service an asset just before it is likely to fail, rather than on a fixed schedule or after a breakdown. Building this well requires reliable sensor data pipelines feeding a model, not just a maintenance calendar.
Key takeaways
- Services assets based on condition, not a fixed schedule
- Cuts unplanned downtime and avoids servicing healthy equipment
- Reliable sensor data and failure history outweigh model complexity
- Start with a few critical, well-instrumented assets
- Predictions must flow into work orders to change behavior
What Predictive Maintenance means
Predictive maintenance is the practice of servicing an asset just before it is likely to fail, guided by its actual condition rather than a fixed calendar or a breakdown. Instead of replacing a bearing every six months whether it needs it or not, you watch its vibration, temperature, or other signals and act when the data says failure is approaching. Done right, it reduces both unplanned downtime and the waste of servicing healthy equipment.
Making it work depends far more on the data pipeline than on the model. You need sensors that reliably capture the right signals, a path to move that data off the floor, and enough history of past failures to know what a failure actually looks like in the data. The analysis can range from simple threshold rules to machine learning, but a sophisticated model on thin or noisy data will simply produce confident false alarms.
The most common failure mode when building this is starting with the algorithm instead of the outcome. It pays to pick a small number of critical, well-instrumented assets, prove that predictions genuinely prevent failures, and connect those predictions into the maintenance workflow so a warning becomes a work order. Predictive maintenance that lives only in a dashboard, disconnected from the CMMS, rarely changes behavior.
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Where Predictive Maintenance shows up in how we build.