The real challenge today in facilities maintenance isn’t whether to be proactive, but it’s deciding how to be proactive. Preventive, condition-based, and predictive maintenance all aim to reduce downtime and manage risk. But they rely on different triggers, different levels of data, and different levels of insight. Understanding those differences is what separates routine maintenance from strategic asset management.
What Is Preventive Maintenance?
Preventive maintenance (PM) is the most traditional form of proactive maintenance. Tasks are scheduled based on time intervals (e.g., weekly, monthly, annually) or usage metrics (e.g., run hours, cycles), regardless of whether the asset shows signs of wear.
Think of it like scheduled oil changes or routine inspections. It’s based on expected wear patterns and historical averages.
In preventive maintenance, the trigger is simple: it’s time.
Why teams use it:
- Easy to plan and budget
- Works well for assets with predictable wear
- Supports regulatory or compliance requirements
- Reduces purely reactive breakdowns
Limitations:
- Can result in unnecessary work
- Doesn’t catch random or atypical failures
- Ignores real-time asset condition
In many organizations, preventive maintenance forms the backbone of reliability programs because it’s structured and familiar. But if it’s applied blindly, you’re still reacting to assumed life expectancy rather than actual equipment behavior.
What Is Condition-Based Maintenance?
Condition-based maintenance (CBM) shifts the decision from the calendar to the state of the asset.
Instead of acting because “it’s time,” you monitor equipment through inspections, meters, or sensors and intervene when a predefined condition or threshold is breached.
In condition-based maintenance, the trigger changes: something measurable has shifted.
For example:
- A filter is replaced when pressure exceeds a limit
- A motor is serviced when vibration crosses an alarm level
- A component is inspected when temperature rises beyond tolerance
Why teams use it:
- Reduces unnecessary time-based work
- Focuses labor on assets showing measurable degradation
- Extends asset life compared to rigid schedules
Limitations:
- Reacts once a threshold is reached
- Requires consistent condition monitoring
- Depends on clearly defined limits and response processes
CBM is more precise than schedule-only PM because it uses asset health to guide decisions. But it still responds once warning signs appear, instead of forecasting when failure is likely, like predictive maintenance does.
What Is Predictive Maintenance?
Predictive maintenance (PdM) builds on condition monitoring by analyzing trends over time.
Instead of waiting for a threshold to be crossed, predictive maintenance evaluates performance patterns and rates of change to estimate when failure is likely and before it impacts operations.
Here, the trigger shifts again: data trends indicate rising risk.
While condition-based maintenance asks, “Is something wrong now?”, predictive maintenance asks, “based on the data pattern, when will this become a problem,” and it gives you time to plan.
Why teams use it:
- Provides earlier warning rather than threshold-based alerts
- Enables planned downtime instead of emergency repairs
- Improves labor allocation and capital planning
Limitations
- Requires strong historical data
- Depends on consistent failure tracking
- Not every asset justifies predictive investment
Predictive maintenance is forward-looking. It anticipates failure rather than reacting to it.
Where AI Fits Into Predictive Maintenance
As data maturity improves, many organizations layer artificial intelligence into predictive maintenance programs. AI models can analyze large volumes of historical work orders, condition readings, and operating data to identify patterns.
Rather than replacing maintenance strategy, AI strengthens it by helping teams detect subtle degradation, refine failure forecasts, and prioritize work based on probability and impact.
To learn more about how AI supports facilities strategy, see our guide on AI for Facilities Management.
Comparing the Three Approaches
| Preventive Maintenance | Condition-Based Maintenance | Predictive Maintenance | |
| Primary Trigger | Time or usage interval | Threshold exceeded | Forecasted failure risk |
| Core Question | “Is it time?” | “Is something wrong now?” | “When will it fail?” |
| Data Requirement | Asset age, install date, manufacturer intervals, run hours, maintenance schedules | Vibration readings, temperature data, pressure levels, flow rates, inspection results, predefined alarm thresholds | Historical sensor trends, time-series condition data, failure codes, work order history, MTBF trends |
| Decision Logic | Calendar-driven | Condition-driven | Trend-driven |
| Planning Flexibility | Fixed schedule | Limited (reacts to alarms) | High (plans before failure) |
| Best For | Predictable wear, compliance | Measurable degradation | Critical systems with high downtime cost |
| Risk Visibility | Basic | Moderate | Advanced |
A Practical Reminder: Preventive Maintenance Isn’t Always the Right Choice
Preventive maintenance isn’t always the smartest financial choice.
For one Nuvolo customer, their facilities team was performing routine preventive maintenance on a group of sump pumps. When they evaluated the full cost, including labor, parts, technician time, they found it cost more to maintain the pumps on schedule than to replace them when they failed.
Because the pumps were inexpensive and easy to swap out, a run-to-failure strategy reduced costs without increasing operational risk.
The lesson is simple: maintenance strategy should match asset consequences.
Low-cost, low-impact assets may not justify structured preventive work. High-risk systems absolutely do.
Conclusion
Preventive, condition-based, and predictive maintenance all aim to reduce downtime and manage risk. The difference lies in how decisions are triggered and how much foresight they provide.
- Preventive maintenance is time-based.
- Condition-based maintenance is threshold-based.
- Predictive maintenance is trend-based.
A mature maintenance program doesn’t rely on just one strategy. It applies the right level of insight to the right asset, supported by strong data and clear risk visibility.
That’s what turns proactive maintenance into strategic asset management.