The Quick Answer
Preventive maintenance is scheduled at fixed intervals — change the oil every 3 months, replace the bearing every 6 months. It's better than run-to-failure but still wastes money: you either maintain too early (wasting useful life) or too late (missing degradation that accelerated between intervals).
Predictive maintenance uses sensor data and machine learning to detect degradation in real time. You maintain exactly when needed — not too early, not too late. It costs more to deploy but saves 6x in maintenance costs and reduces unplanned downtime by 72%.
Use preventive for low-criticality equipment where failure is cheap and predictable. Use predictive for critical rotating equipment where failure costs $100K+ and degradation patterns are complex.
Side-by-Side Comparison
| Dimension | Preventive Maintenance | Predictive Maintenance |
|---|---|---|
| How it works | Scheduled at fixed time intervals regardless of condition | Sensors + ML detect degradation; maintenance triggered by actual condition |
| Cost per event | $200K–$400K (scheduled but may be unnecessary) | ~$144K (only when needed) |
| Unplanned downtime | Reduced but not eliminated — failures between intervals still occur | 72% reduction (digital twin simulation) |
| False alarms | N/A — maintenance is scheduled regardless | Zero false alarms on cross-dataset transfer test |
| Over-maintenance | High — replaces parts that still have useful life | Minimal — parts replaced only when degrading |
| Under-maintenance | Moderate — fast degradation between intervals missed | Minimal — continuous monitoring catches acceleration |
| Sensor requirement | None | Vibration + temperature sensors on monitored equipment |
| Data requirement | Manufacturer schedules | 2–4 weeks baseline data + historical failure records |
| Deployment time | Days — set schedule and execute | Weeks — install sensors, collect baseline, train model |
| Upfront cost | Low | Moderate (sensors + software + integration) |
| Annual savings | 20–30% vs run-to-failure | 72–86% vs run-to-failure |
| Best for | Low-criticality, predictable equipment | Critical rotating equipment, high-cost-of-failure assets |
| Detection lead time | None — scheduled, not detected | 5–24 days before failure |
When to Use Each
Use Preventive Maintenance When:
- Equipment failure is cheap (under $10K per event)
- Failure pattern is predictable and time-based
- Equipment is low-criticality (no production impact)
- You don't have sensors or data infrastructure
- Budget for sensors and software is not available
- Equipment has known wear-out patterns (filters, seals)
Use Predictive Maintenance When:
- Equipment failure costs $100K+ per event
- Failure pattern is complex and variable
- Equipment is critical to production
- You can install vibration/temperature sensors
- You can collect 2–4 weeks of baseline data
- Equipment is rotating machinery (pumps, compressors, generators)
The Hybrid Approach
Most facilities shouldn't choose one or the other — they should use both. The optimal strategy is tiered:
- Tier 1 (Critical): Predictive maintenance on high-impact rotating equipment (export pumps, compressors, generators) where failure costs $100K+
- Tier 2 (Important): Preventive maintenance on medium-criticality equipment with predictable wear patterns
- Tier 3 (Low priority): Run-to-failure on low-cost, easily replaceable equipment where monitoring isn't economically justified
This tiered approach concentrates monitoring investment where it matters most, while keeping costs low for equipment where simple strategies suffice.
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