Predictive Maintenance
Predictive maintenance (PdM) uses real-time data from sensors, IoT devices, and analytics to forecast when an asset is likely to fail, enabling maintenance to be performed just before a breakdown occurs. Techniques include vibration analysis, oil analysis, thermal imaging, and machine-learning models trained on historical failure data. It represents the most advanced tier of proactive maintenance strategies.
Why it matters
Predictive maintenance eliminates both unnecessary scheduled servicing and costly unplanned breakdowns by targeting the optimal intervention point. Organisations that adopt PdM typically see a 25–30% reduction in maintenance costs and a significant decrease in unplanned downtime. It also extends useful asset life by avoiding both over-maintenance and neglect.
How MapTrack helps
MapTrack integrates with IoT sensors and OEM telematics feeds to surface condition data alongside asset records, helping teams move from calendar-based to condition-based maintenance decisions.
Frequently asked questions
What data is needed for predictive maintenance?
Effective predictive maintenance requires reliable condition-monitoring data such as vibration levels, temperature, pressure, oil quality, or electrical readings. It also benefits from historical failure and maintenance records. The quality and consistency of data collection is more important than the volume of data points.
Is predictive maintenance practical for small fleets?
Modern cloud-based platforms and affordable IoT sensors have made predictive maintenance accessible even for smaller operations. Starting with a few critical assets that have high failure costs allows teams to prove value before scaling. Many organisations begin with condition-based thresholds and gradually layer in more advanced analytics.
Related terms
Condition-Based Maintenance
Condition-based maintenance (CBM) is a strategy that triggers maintenance actions based on the actual measured condition of an asset rather than fixed time intervals. Condition indicators may include vibration levels, temperature, pressure, fluid analysis results, or visual inspections. It sits between simple preventive maintenance and fully predictive maintenance on the maturity spectrum.
IoT Sensors
IoT (Internet of Things) sensors are connected devices that collect and transmit data about an asset’s condition, environment, or usage in real-time. Common sensor types measure temperature, vibration, humidity, fuel levels, engine hours, pressure, and tilt. The data is transmitted wirelessly to a central platform for monitoring, alerting, and analysis.
OEM Telematics
OEM telematics refers to the factory-installed tracking and diagnostic systems built into vehicles, plant, and heavy equipment by the original equipment manufacturer. These systems collect and transmit data including GPS location, engine hours, fuel consumption, fault codes, idle time, and operating parameters. Major OEMs such as Caterpillar, Komatsu, John Deere, Volvo, and Hitachi each offer proprietary telematics platforms.
Preventive Maintenance
Preventive maintenance (PM) is a proactive maintenance strategy in which assets are serviced at predetermined time or usage intervals to reduce the likelihood of failure. Tasks may include inspections, lubrication, filter changes, calibrations, and component replacements. PM schedules are typically based on manufacturer recommendations, regulatory requirements, or historical failure data.
Mean Time Between Failures (MTBF)
Mean Time Between Failures (MTBF) is a reliability metric that measures the average elapsed time between inherent failures of a repairable system during normal operation. It is calculated by dividing the total operational time by the number of failures over a given period. MTBF is typically expressed in hours and is used to compare the reliability of assets, components, or equipment models.
See how MapTrack handles predictive maintenance