Condition-based maintenance (CBM) is a maintenance strategy that triggers service and inspection based on the actual condition of equipment rather than fixed time or usage intervals. Instead of scheduling maintenance on a calendar, CBM uses real-time or periodic monitoring data — vibration levels, oil analysis results, thermal readings, pressure measurements — to determine when a specific asset actually needs attention.
The core principle is straightforward: maintain equipment when evidence indicates it is needed, not because a date on a schedule says so. This prevents both premature maintenance on equipment that is performing normally and delayed maintenance on equipment that is degrading faster than expected.
CBM is typically layered on top of a preventive maintenance foundation. Most reliability programs use PM as the baseline and apply CBM selectively to high-criticality assets where monitoring investment is justified by failure consequence.
Why Condition-Based Maintenance Matters
Unplanned equipment failures in asset-intensive operations are rarely instantaneous. Most failures develop over time — bearings degrade, oil oxidizes, alignment drifts, insulation breaks down. CBM exists to detect these degradation signals before they become failure events.
The cost differential between a planned CBM intervention and an unplanned failure is substantial. A planned bearing replacement on a crusher costs labor and parts. An unplanned bearing seizure on the same asset costs emergency labor, expedited parts, collateral damage to adjacent components, and production loss that can run into tens or hundreds of thousands of dollars depending on the operation.
CBM also eliminates a hidden cost in traditional PM programs: unnecessary maintenance. Replacing components on a fixed schedule regardless of their actual condition wastes parts, labor, and planned downtime windows. CBM directs maintenance resources to assets that actually need them.
How It Works in Practice
CBM programs are built around three core activities: monitoring, analysis, and intervention.
Condition Monitoring
Monitoring collects the data that drives CBM decisions. Common monitoring techniques include:
- Vibration analysis: Detects imbalance, misalignment, bearing wear, and looseness in rotating equipment. Accelerometers measure vibration signatures and compare them against baseline or alarm thresholds.
- Oil analysis: Evaluates lubricant condition and identifies wear particles, contamination, and fluid degradation. Particularly valuable for gearboxes, hydraulic systems, and engines.
- Thermal imaging: Identifies heat anomalies in electrical systems, motors, and mechanical components that indicate developing faults.
- Ultrasonic testing: Detects leaks, electrical discharge, and early-stage bearing defects through high-frequency sound signatures.
- Motor current analysis: Identifies electrical and mechanical faults in motors without physical contact.
Data Analysis and Threshold Setting
Raw monitoring data is only useful when compared against meaningful thresholds. CBM programs establish baselines for each asset — what normal looks like — and set alarm levels that trigger further investigation or maintenance action. Thresholds are typically derived from OEM specifications, industry standards, and historical failure data for the specific asset class.
Trend analysis is more powerful than point-in-time measurements. A single vibration reading may be inconclusive. A vibration reading that has increased 40 percent over 60 days is a clear degradation signal regardless of whether it has crossed an absolute threshold.
Intervention Decision and Planning
When condition data crosses a threshold or trend analysis indicates approaching failure, a maintenance work order is generated. The advantage over reactive maintenance is lead time — CBM typically provides days to weeks of advance notice, allowing parts to be staged, labor to be scheduled, and production to be planned around the maintenance window.
The intervention decision should always weigh failure probability against failure consequence. High-consequence assets with moderate degradation signals often warrant earlier intervention than low-consequence assets showing the same data pattern. Asset Criticality Ranking (ACR) provides the framework for making these decisions consistently.
Condition-Based Maintenance by Industry
Manufacturing: High-speed production lines depend on motors, gearboxes, pumps, and compressors that run continuously with limited maintenance windows. CBM on these assets — particularly vibration monitoring and oil analysis — detects developing faults between planned shutdowns and allows maintenance to be scheduled during the next available window rather than in response to a breakdown that stops the line.
Mining: Haul truck wheel motors, crusher bearings, conveyor drives, and hydraulic systems operate under extreme loads in high-contamination environments. CBM is standard practice for these assets because failure consequences are severe — a single crusher failure can halt an entire processing circuit. Oil analysis programs in mining often run on weekly or monthly cycles to track wear metal trends in high-value drivetrain components.
Oil and Gas: Compressors, turbines, and pumps in upstream and midstream operations are often remote, difficult to access, and subject to regulatory inspection requirements. CBM using continuous vibration monitoring and periodic oil sampling allows operators to maximize run time between planned outages while maintaining the condition evidence required for regulatory compliance and warranty defense.
Crane and Rigging: Wire rope condition monitoring, hydraulic system pressure analysis, and structural load monitoring are CBM applications in crane operations where component failure has direct safety consequences. Condition data also supports the inspection documentation requirements that crane operations must maintain for regulatory compliance.
Common CBM Program Failures
Monitoring without analysis: Organizations install sensors and collect data but lack the process or expertise to interpret trends and set meaningful thresholds. Data accumulates without driving decisions. CBM requires analytical capability, not just monitoring hardware.
Threshold errors: Alarm thresholds set too conservatively generate false positives that erode technician trust in the system. Thresholds set too loosely miss genuine degradation signals. Thresholds should be calibrated from historical failure data for each specific asset class, not copied from generic tables.
No integration with the work order system: CBM findings that do not automatically generate work orders create a gap between detection and action. By the time a monitoring alert is manually communicated to the maintenance scheduler, the lead time advantage may be lost. CBM data should integrate directly with the CMMS to trigger work orders automatically at defined thresholds.
Applying CBM to the wrong assets: CBM monitoring requires investment — sensors, analysis tools, technician time. Applying it uniformly across all assets regardless of criticality produces poor ROI. CBM delivers the highest return on high-criticality assets where failure consequence justifies the monitoring cost.
Abandoning baselines after personnel changes: CBM programs depend on institutional knowledge of what normal looks like for each asset. When experienced technicians leave and baseline data is not documented, the program loses its reference point and thresholds become arbitrary.
CBM vs. Other Maintenance Strategies
- Corrective maintenance: Repair after failure. No monitoring, no prediction. Appropriate only for non-critical assets where failure consequence is low. See: Corrective Maintenance.
- Preventive maintenance: Scheduled service based on time or usage intervals regardless of condition. The operational foundation most reliability programs build on. See: Preventive Maintenance (PM).
- Condition-based maintenance: Service triggered by actual equipment condition data. Eliminates unnecessary maintenance and provides advance warning of developing failures.
- Predictive maintenance: Uses advanced analytics and machine learning to forecast failure timing from condition data. CBM identifies that a problem exists; predictive maintenance estimates when failure will occur. See: Predictive Maintenance (PdM).
Frequently Asked Questions
What is the difference between condition-based maintenance and predictive maintenance?
Condition-based maintenance triggers a maintenance action when monitoring data crosses a defined threshold — indicating that a problem exists now. Predictive maintenance uses advanced analytics to forecast when a failure will occur, giving teams a projected failure date rather than just an alarm. CBM answers the question “does this asset need attention?”; predictive maintenance answers “when will this asset fail if we do nothing?” Most operations implement CBM first and layer predictive capabilities on top as their data maturity increases.
How do you implement a condition-based maintenance program?
Start by identifying high-criticality assets using an Asset Criticality Ranking process. For each asset, select monitoring techniques appropriate to its failure modes — vibration analysis for rotating equipment, oil analysis for lubricated systems, thermal imaging for electrical components. Establish baselines during normal operation, set thresholds based on OEM data and historical failures, and integrate monitoring alerts with your CMMS work order system. Begin with 10 to 20 assets, validate the process, then scale.
Is condition-based maintenance more expensive than preventive maintenance?
CBM requires higher upfront investment — monitoring equipment, analysis tools, and technician training. However, it typically reduces total maintenance cost by eliminating unnecessary PM work, extending component life by servicing only when needed, and preventing the high cost of unplanned failures. The ROI case is strongest for high-criticality assets with expensive components or high failure consequences. For low-criticality assets, the monitoring investment often does not justify the return.
What sensors are used in condition-based maintenance?
The most common CBM sensors are accelerometers for vibration analysis, thermocouples and infrared cameras for thermal monitoring, and oil sampling ports for lubricant analysis. Ultrasonic sensors detect high-frequency sound signatures associated with leaks and bearing defects. Current transducers measure motor electrical signatures. Sensor selection is driven by the failure modes of the specific asset — a gearbox monitored primarily through oil analysis requires different instrumentation than a motor monitored through vibration and current analysis.
Related Terms
- Preventive Maintenance (PM)
- Predictive Maintenance (PdM)
- Asset Criticality Ranking (ACR)
- Mean Time Between Failures (MTBF)
- Corrective Maintenance
- Failure Mode and Effects Analysis (FMEA)
- Probability of Failure (POF)
Build a Smarter Maintenance Program With Redlist
See how Redlist helps industrial maintenance teams implement condition-based maintenance — integrating monitoring data with work order management to close the gap between detection and action.
