Predictive maintenance is a strategy in which maintenance is triggered by evidence that an asset is starting to degrade, rather than by a fixed calendar or by a breakdown. Condition data such as vibration, temperature, current draw, run hours and fault history is monitored, and work is planned for the window between the first sign of a problem and actual failure.
Where predictive maintenance sits
Maintenance strategies sit on a scale. Reactive maintenance waits for the failure. Planned preventive maintenance services the asset at fixed intervals whether it needs it or not. Predictive maintenance tries to do the work only when the asset's condition says it is due, so fewer services are wasted and fewer failures are missed.
Condition-based maintenance is the simpler form: a threshold is set, such as a bearing temperature or a vibration level, and crossing it raises a work order. Predictive maintenance in the stricter sense goes a step further and uses the trend, often across many similar assets, to estimate how long remains before failure.
The P-F interval
The idea behind it is the P-F interval: the time between the point where a developing fault can first be detected (P) and the point of functional failure (F). A chiller bearing may show rising vibration weeks before it seizes. If you detect the fault early in that interval, the repair can be planned, parts ordered and the shutdown scheduled outside peak cooling hours.
The interval also sets the inspection frequency. If a fault is detectable six weeks before failure, checking every two weeks gives two or three chances to catch it; checking every quarter will usually miss it.
What it needs in practice
- A reliable asset register, so every reading and work order lands on the right asset. Asset tagging is usually the first step
- Condition data: BMS points, meter readings, IoT sensors, or readings captured by technicians on rounds
- Clean failure history, with closed work orders that record the actual cause, not just "fixed"
- Rules or models that turn readings into action, and a CAFM or CMMS that raises the work order automatically
- Somebody accountable for acting on the alerts, or they become noise
Where it pays in GCC buildings
Predictive maintenance earns its cost on critical, expensive assets whose failure hurts: chillers and cooling towers through the summer, pumps, lifts and escalators in malls and towers, generators and UPS in hospitals and data rooms. For a light fitting or a door closer, a simple preventive or run-to-failure approach is usually cheaper.
Most portfolios end up with a mix: predictive for a short list of critical plant, preventive for statutory and life-safety assets, and reactive for low-impact items. Reflexion AI is our approach to the predictive part, combining work order history with sensor and meter readings.
Common questions
What is the difference between predictive and preventive maintenance?
Preventive maintenance is done at fixed intervals, such as every three months, whatever the asset's condition. Predictive maintenance is done when condition data shows the asset is starting to degrade. Preventive is simpler to run; predictive avoids unnecessary services and catches faults that develop between scheduled visits.
Is condition-based maintenance the same as predictive maintenance?
They are closely related and often used interchangeably. Condition-based maintenance usually means acting when a reading crosses a set threshold. Predictive maintenance usually means using the trend in the data to estimate when failure will occur, so work can be planned further ahead.
Do you need IoT sensors for predictive maintenance?
Not always. Many buildings already have useful data in the BMS, energy meters and work order history. Readings captured by technicians on routine rounds, such as vibration or thermal scans, also work. Dedicated sensors make sense for critical assets where continuous monitoring justifies the cost.
Which assets should get predictive maintenance first?
Start with assets whose failure is costly or disruptive and which give warning before they fail: chillers, large pumps, cooling towers, lifts, generators and UPS systems. Assets that fail without warning, or are cheap to replace, rarely justify it.