Predictive Maintenance
Using equipment condition data to decide when maintenance is actually needed.
Quick Answer
Predictive maintenance monitors the actual condition of equipment, using measurements such as vibration, temperature, and energy draw, and applies analytics to forecast when a failure is likely. Maintenance is then scheduled just before it is needed, reducing both unplanned downtime and unnecessary routine servicing.
The Full Picture
Predictive maintenance replaces the calendar with evidence. Instead of servicing a pump every six months regardless of its state, a predictive approach measures signals that change as equipment degrades, such as vibration, bearing temperature, motor current, or oil condition, and acts when those signals drift beyond normal ranges.
Techniques range from simple to advanced. Technicians have long used handheld tools such as vibration analyzers and infrared cameras during inspections. Newer systems stream data from permanently installed sensors or from building automation systems into software that applies thresholds, trend analysis, or machine learning models to estimate remaining useful life and flag anomalies.
The approach works best on critical or costly equipment where failure is expensive, such as chillers, large pumps, and air handlers in hospitals or data centers. It requires sensors, reliable data, enough history to establish a baseline, and a process to turn alerts into work orders. For simple low-cost equipment, scheduled or run-to-failure strategies are often more economical.
Results depend on data quality and on whether the organization acts on the alerts. Facilities teams commonly integrate predictive alerts with a CMMS so that a detected issue becomes a tracked work order. Buildings designed with metering and sensor points in mind make these programs easier, which is one reason owners ask for monitoring provisions during design.
Real Examples
Common Misconceptions
People assume: Predictive maintenance can foresee any failure.
Actually: It can only detect failure modes that produce measurable warning signs, and its accuracy depends on sensor coverage, data quality, and history. Some failures still occur without warning.
People assume: Predictive maintenance requires AI.
Actually: Many effective programs use threshold alarms and trend analysis. Machine learning can help on complex data, but it is one tool among several.
Frequently Asked Questions
How is predictive maintenance different from preventive maintenance?
Preventive maintenance follows a fixed time or usage schedule. Predictive maintenance is triggered by measured equipment condition and forecasts when service will be needed.
What data does predictive maintenance use?
Common inputs include vibration, temperature, pressure, motor current, energy consumption, runtime hours, and data from building automation systems.
Is predictive maintenance worth it for every building?
Not always. It tends to pay off on critical, expensive equipment where unplanned failure is costly. For low-value or easily replaced equipment, simpler strategies are often more economical.
What is condition-based maintenance?
Condition-based maintenance performs service when measurements show a defined condition, such as a threshold being crossed. Predictive maintenance goes a step further by using trends or models to forecast when that will happen.