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.
Why it matters
CBM avoids the waste of servicing assets that are still in good condition while catching deterioration before it causes failure. This approach is particularly valuable for assets where failure consequences are severe but failure patterns are not purely time-dependent. It also reduces spare parts consumption and technician workload compared to rigid calendar-based schedules.
How MapTrack helps
MapTrack supports condition-based maintenance by allowing teams to record meter readings, inspection results, and sensor data that automatically trigger work orders when thresholds are breached.
Frequently asked questions
How does condition-based maintenance differ from preventive maintenance?
Preventive maintenance occurs at fixed intervals (e.g., every 500 hours or every 90 days) regardless of the asset’s actual state. Condition-based maintenance only triggers when a measured condition indicator, such as a vibration reading or oil analysis result, reaches a defined threshold. CBM can reduce unnecessary maintenance while still preventing failures.
What are common condition indicators used in CBM?
Common indicators include vibration amplitude, operating temperature, oil contamination levels, pressure readings, electrical current draw, and visual wear measurements. The best indicator depends on the asset type and its typical failure modes. Many organisations start with simple periodic inspections and progress to continuous sensor monitoring over time.
Related terms
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.
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.
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.
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.
Service History
Service history is the chronological record of all maintenance, repairs, inspections, and modifications performed on an asset throughout its lifecycle. A comprehensive service history includes dates, descriptions of work, parts used, technician details, costs, and supporting documentation such as photos or test certificates. It serves as the permanent maintenance biography of an asset.
See how MapTrack handles condition-based maintenance