Predictive vs preventive maintenance: differences and when to use each
Comparison table of predictive vs preventive maintenance: costs, downtime, data requirements, and how to move from one to the other with no extra hardware.
Predictive vs preventive maintenance
Preventive maintenance services equipment at fixed intervals (every X days or usage hours), whether it is failing or not. Predictive maintenance analyzes each machine's actual condition — vibration, temperature, pressure, energy — and predicts when it will fail, so you only intervene when needed.
Comparison table
| Criterion | Preventive | Predictive |
|---|---|---|
| When it intervenes | Fixed calendar (every 30/60/90 days) | When actual condition warrants it |
| Data required | None (just a calendar) | Machine signals (PLC, sensors) |
| Parts replaced | Healthy ones too | Only degrading ones |
| Unplanned downtime | Still happens between services | Anticipated hours or days ahead |
| Labor cost | High (unnecessary interventions) | Optimized (justified intervention) |
| Main risk | Over-maintenance and surprise failures | Requires quality data |
| Maturity required | Low | Medium — with the right tool, low |
Why preventive alone is not enough
A well-run preventive plan reduces failures, but carries two hidden costs:
- Over-maintenance. Healthy bearings, belts and filters get replaced because "it was due". Every unnecessary intervention costs parts, technician hours and human-error risk on reassembly.
- Failures don't respect the calendar. A compressor can degrade in 3 weeks due to contaminated refrigerant; the quarterly plan won't see it. Surprise stops keep happening right between two services.
Predictive attacks both: it intervenes only when data shows degradation, and warns before degradation becomes downtime.
What do I need to go predictive?
The classic objection is "I need new sensors and years of history". With the current generation of tools, neither is true:
- No extra hardware. The PLCs already controlling your machines (Siemens S7, Allen-Bradley, Schneider) already measure temperature, pressure, cycles and consumption. Rela AI reads them directly over Modbus TCP, S7comm, EtherNet/IP, OPC UA or MQTT through a secure VPN.
- No failure history. The system learns each machine's baseline during its first connected weeks and detects deviations from then on. See predictive maintenance without history.
How Rela AI does it
- Connects your existing PLCs (without touching the PLC program).
- Learns each metric's baseline and computes every asset's health (AHI).
- Predicts remaining useful life (RUL) and triggers condition-based interventions (CBM).
- Alerts on WhatsApp with the diagnosis and a proposed work order — and the technician replies in natural language.
Preventive doesn't disappear: calendar plans remain right for regulatory tasks (lubrication, HACCP inspections). The optimum is a mix: preventive for compliance, predictive for what's expensive. Rela AI manages both in the same maintenance module.
FAQ
Does predictive replace preventive? Not entirely: regulatory and low-criticality tasks stay on calendar. Predictive applies first to the equipment whose downtime costs most.
How long until it delivers value? First baselines are learned in 1–3 weeks of data. First actionable alerts usually arrive within the first month.
What if my plant is small? Per-asset pricing (from 10 machines) is built for SMEs: see pricing.
Mobile App
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