Predict equipment failure from sensor data
Last updated 11 August 2026
Predictive maintenance lets plant teams schedule a repair before a machine fails, by learning each asset’s normal operating signature from sensor and control data already being recorded and flagging drift toward known failure modes, typically converting a share of unplanned stoppages into planned work rather than eliminating breakdowns outright.
| Dimension | Score | What that means |
|---|---|---|
| Impact | 4/5 | Material gain across a function |
| Effort | 4/5 | Custom build, about a quarter |
| Data readiness | 4/5 | Needs history across two systems |
| Company size | 201–1000 · 1000+ | |
What problem this solves
A production line stops when a motor, bearing or conveyor fails without warning. Maintenance crews arrive after the fact, the plant loses output for hours, and the part that failed often takes weeks to arrive. Fixed-interval servicing helps, but it replaces healthy components on a calendar and still misses the failures that matter.
Most plants already record vibration, temperature, current draw and cycle counts. The data sits in a historian nobody queries between incidents.
How it works
- Pull historical sensor and control data for the assets that actually cause lost output, not the whole plant.
- Label past failures from maintenance work orders so the model has examples of what preceded a breakdown.
- Train a model to learn each asset’s normal operating signature across load, speed and ambient conditions.
- Score live data continuously and raise an alert when readings drift toward a pattern that preceded past failures.
- Route the alert into the existing maintenance planning system as a work order with the supporting evidence attached.
- Track which alerts led to a genuine finding and retrain, because a model that cries wolf gets switched off within a month.
What you need to start
- One to two years of historian data sampled fast enough to capture the failure mode, not daily averages
- Maintenance work-order history with dates and causes, so past failures can be labelled
- An asset criticality list — this is worth building only where a stoppage is expensive
- A maintenance planner who will act on alerts and record whether each one was real
Expected outcomes
| Metric | Typical range | Source |
|---|---|---|
| Assembly disruption avoided at one plant | ~500 minutes per year | View source |
| Reduction in downtime (industry surveys) | 35–45% for PdM programmes | View source |
| Reduction in maintenance costs | 25–30% for PdM programmes | View source |
Real-world signal
BMW Group Plant Regensburg reported that an AI system monitoring conveyor technology from existing component and control data, with no additional sensors, avoids around 500 minutes of assembly disruption a year and covers about 80% of main assembly lines.
BMW Group · 2023
Common questions
How much data do you need to start?
Enough history to contain real failures. One to two years of sensor data plus matching work orders is a workable floor. If an asset has never failed in your records the model has nothing to learn from, and you are better off starting with one that has.
What happens when the model raises a false alarm?
You lose a planner’s time, and you lose trust. Track the hit rate from day one and tune the alert threshold deliberately. A system that flags everything is worse than no system, because crews stop reading it.
How long does it take to get running?
Getting data out of the historian and matching it to work orders usually takes longer than the modelling. A single asset class is a realistic first target; plant-wide coverage is a multi-year programme.
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