Sensor-based vs analytics-based predictive maintenance
Sensor-based predictive maintenance adds condition sensors to specific machines — fast and accurate on rotating equipment, but costs per machine. Analytics-based models existing historian and SCADA data to cover many assets without new sensors — better for scale, but dependent on data quality.
The two approaches
There are two broad ways to do predictive maintenance, and the difference is where the data comes from.
| Sensor-based | Analytics-based | |
|---|---|---|
| Data source | Added condition sensors | Existing historian / SCADA / CMMS |
| Best on | Rotating equipment | Many assets, process plant |
| Deploy speed | Fast per machine | Depends on data quality |
| Cost driver | Per-machine hardware | Software + data work |
Strengths and trade-offs
Sensor-based platforms are fast to deploy and excellent on rotating equipment because they measure exactly the right signals (vibration, temperature) — but per-machine cost grows with scale. Analytics-based platforms cover many assets without new hardware by modelling data you already collect — better for scaling across a large estate, but only as good as that existing data. Neither is universally 'better'; they suit different problems.
Why many plants use both
The common, pragmatic answer is to combine them: put sensors on the critical rotating assets where early, accurate detection matters most, and use analytics across the wider estate to catch drift on assets that don't justify dedicated sensors. Start with the critical few on sensors, prove the value, then extend coverage with analytics.
Frequently asked questions
What is the difference between sensor-based and analytics-based predictive maintenance?
Sensor-based adds condition sensors to specific machines and diagnoses from that data — fast and accurate on rotating equipment but priced per machine. Analytics-based models existing historian, SCADA and maintenance data to cover many assets without new sensors — better for scale but dependent on data quality.
Which is better, sensors or analytics for predictive maintenance?
Neither universally. Sensors are best for early, accurate detection on critical rotating equipment; analytics scale more cheaply across many assets using data you already have. Many plants combine them — sensors on the critical few, analytics across the wider estate.
Do I need new sensors for predictive maintenance?
Not always. Sensor-based platforms need added hardware, but analytics-based platforms model existing historian and SCADA data to cover many assets without new sensors. The right choice depends on which assets are critical and how good your existing data is.
Related guides
How much does predictive maintenance cost?
Predictive maintenance cost has three parts: monitoring hardware (for sensor-based approaches, priced per asset), software or analytics (often per-asset or per-site subscription), and the people-time to act on findings. Analytics on existing data scales cheaper than sensors on every machine.
Predictive maintenance: a practical guide
What predictive maintenance is, how it differs from preventive maintenance, which techniques fit which assets, and how to start without boiling the ocean.
AI agents for industrial maintenance
AI agents are software that can reason over plant data and take or recommend multi-step actions — triaging alerts, drafting work orders, searching manuals. What they realistically do for maintenance today, where they help, and how to start safely.
Software that helps
Augury
Machine health monitoring for rotating equipment using vibration and AI.
AVEVA Predictive Analytics
Early-warning analytics for critical process and power assets.
Cognite Data Fusion
Industrial DataOps and digital-twin foundation.