Maintenance teams have traditionally relied on scheduled servicing or reacted to equipment failures. While these approaches remain useful, they do not always reflect the actual condition of an asset.
A piece of equipment may be inspected or serviced simply because a predefined date has arrived, even if it is operating normally. Conversely, an asset may deteriorate between scheduled inspections and fail before its next planned intervention.
Condition-Based Maintenance (CBM) offers a different approach.
Instead of scheduling maintenance solely according to time or usage, condition-based maintenance uses information about the actual condition of an asset to determine when intervention is required.
By monitoring indicators such as temperature, vibration, pressure, energy consumption or equipment performance, maintenance teams can identify changes that may indicate deterioration and take action before a failure occurs.
Condition-Based Maintenance therefore sits between traditional preventive maintenance and more advanced predictive maintenance strategies. It enables organisations to make maintenance decisions based on evidence rather than fixed schedules or assumptions.
Condition-Based Maintenance is a maintenance strategy in which maintenance activities are triggered by the measured condition or performance of an asset.
The basic principle is simple:
Maintain the asset when its condition indicates that maintenance is needed.
Instead of asking when an asset was last serviced, maintenance teams focus on questions such as:
The condition of the asset becomes the main factor determining the maintenance decision.
This can help organisations avoid two common problems:
Over-maintenance: performing maintenance on equipment that is still operating correctly.
Under-maintenance: allowing equipment deterioration to continue until a failure occurs.
A typical Condition-Based Maintenance programme follows a continuous cycle.
Not every asset requires continuous condition monitoring.
Organisations should first identify equipment where failure would have significant operational, financial, safety or environmental consequences.
Examples may include:
Asset criticality helps organisations determine where condition monitoring can deliver the greatest value.
The next step is to determine which measurements provide meaningful information about asset health.
Depending on the equipment, these may include:
Different failure modes require different indicators.
For example, vibration analysis can help identify mechanical problems in rotating equipment, while abnormal temperature may indicate electrical or mechanical issues.
Condition information can be collected through periodic inspections or continuously through connected sensors.
Technicians may perform manual inspections using mobile devices, while IoT sensors can automatically transmit data to a central platform.
The choice depends on asset criticality, monitoring requirements and available infrastructure.
Maintenance teams define acceptable operating ranges for each condition indicator.
When a measurement moves outside the defined range, the system can generate an alert or trigger an inspection.
For example:
Normal operating temperature → No action
Temperature approaching threshold → Monitor
Temperature above critical threshold → Inspect equipment
Thresholds should be based on equipment specifications, historical performance, operational requirements and maintenance expertise.
Once an abnormal condition is detected, the maintenance team determines the appropriate response.
This may involve:
The goal is not necessarily to intervene immediately whenever a measurement changes.
Instead, the information helps maintenance teams make a more informed decision.
Condition-Based Maintenance shares the same objective as other proactive maintenance strategies: preventing equipment failures and improving asset reliability. However, the way maintenance decisions are triggered differs.
| Maintenance strategy | What triggers maintenance? | Main approach |
|---|---|---|
| Preventive Maintenance | Time, usage or operating cycles | Maintenance follows a predefined schedule |
| Condition-Based Maintenance | Current asset condition | Maintenance is triggered when condition data indicates deterioration |
| Predictive Maintenance | Predicted future failure | Data and analytics are used to forecast when intervention may be required |
Preventive Maintenance follows predetermined intervals, regardless of the actual condition of the asset.
Inspect the HVAC system every three months.
Condition-Based Maintenance uses information about the asset's condition to determine whether intervention is necessary.
Inspect the HVAC system when vibration or temperature readings indicate abnormal behaviour.
Preventive Maintenance is generally simpler to implement, but it can result in unnecessary interventions when equipment remains in good condition. Condition-Based Maintenance allows organisations to focus resources on assets that show signs of deterioration.
Condition-Based Maintenance uses current or recent condition data to determine whether an asset requires attention.
Predictive Maintenance goes a step further. It combines historical and real-time data with analytics, and increasingly Artificial Intelligence and Machine Learning, to forecast potential failures.
For example:
Condition-Based Maintenance:
"The motor's vibration has exceeded the defined threshold. Inspect the bearing."
Predictive Maintenance:
"Based on the vibration trend and historical failure patterns, the bearing has a high probability of failure within the next 30 days."
The key difference is that Condition-Based Maintenance identifies when an asset's condition requires attention, while Predictive Maintenance estimates when a failure is likely to occur.
Modern Condition-Based Maintenance can rely on a combination of technologies.
Connected sensors provide continuous information about asset condition.
Sensors can measure variables such as:
This eliminates the need to rely exclusively on periodic manual inspections.
Mobile applications allow technicians to record inspection results directly at the point of work.
They can:
This ensures that condition information becomes part of the asset's digital record.
An Enterprise Asset Management platform provides the broader context required to interpret condition data.
Condition measurements can be connected to:
This creates a more complete picture of asset health.
AI and Machine Learning can take Condition-Based Maintenance to the next level.
Instead of relying only on predefined thresholds, algorithms can identify patterns and anomalies across large datasets.
This can help organisations detect changes that may not be immediately visible through conventional threshold-based monitoring.
Monitoring equipment condition can provide early warnings before problems become major failures.
This gives maintenance teams more time to plan interventions and reduce operational disruption.
Maintenance resources can be prioritised according to actual asset condition.
Technicians can focus on equipment that requires attention rather than performing routine interventions on every asset at the same frequency.
Condition-based strategies can reduce unnecessary inspections, component replacements and emergency repairs.
The financial impact can be particularly significant across large asset portfolios.
Continuous or periodic monitoring provides greater visibility into asset health.
Over time, organisations can identify recurring failure patterns and improve maintenance strategies.
Identifying deterioration early can prevent minor problems from becoming major failures.
Timely intervention may reduce equipment stress and extend useful asset life.
Condition data provides an additional layer of information for planning maintenance activities.
Teams can align interventions with operational schedules, spare parts availability and technician capacity.
Condition-Based Maintenance is particularly useful when:
It is less appropriate for very simple, low-cost assets where monitoring costs exceed the potential benefits.
The objective should not be to monitor every asset.
It should be to monitor the right assets.
Temperature, pressure and energy consumption can help identify abnormal operating conditions.
An increase in energy consumption combined with reduced performance may indicate that an HVAC system requires inspection.
Vibration and pressure monitoring can help detect mechanical problems before a pump fails.
Changes in vibration, temperature or electrical current can indicate developing faults.
Condition monitoring can identify changes in equipment performance and support earlier intervention.
Energy, temperature and environmental data can provide insights into the condition and performance of critical building systems.
Organisations should avoid attempting to monitor their entire asset portfolio immediately.
A phased approach is usually more effective.
Identify assets where failures have the greatest operational or financial impact.
Understand how critical assets fail and which condition indicators can provide early warning.
Determine whether manual inspections, sensors or a combination of approaches are appropriate.
Collect sufficient information to understand normal operating conditions.
Establish clear criteria for when an inspection or maintenance intervention should be triggered.
Connect condition information with the organisation's maintenance and asset management system.
Use historical results to refine thresholds, inspection procedures and maintenance strategies.
Despite its benefits, Condition-Based Maintenance can present several challenges.
If asset records are incomplete or inaccurate, condition information becomes harder to interpret.
Thresholds that are too sensitive can generate excessive alerts, while thresholds that are too high may fail to identify problems early enough.
Condition data becomes much more valuable when integrated with maintenance history and asset information.
Maintenance teams may require training to interpret sensor readings and respond appropriately to condition alerts.
Not every asset justifies continuous monitoring. Organisations should evaluate the expected return before deploying sensors.
Condition-Based Maintenance delivers the greatest value when connected to a broader Enterprise Asset Management strategy.
An EAM platform can connect:
Asset → Condition → Alert → Work Order → Intervention → Cost → Maintenance History
This creates a continuous information loop.
The result is a maintenance strategy that becomes increasingly data-driven over time.
For organisations managing assets across multiple sites, this centralised approach can also improve visibility, standardisation and reporting.
Modern EAM platforms can combine asset information, maintenance processes, IoT data and operational analytics in a single environment. Nextbitt's EAM platform, for example, combines physical asset management, work order management and real-time IoT data integration.
Condition-Based Maintenance is a strategy that uses the actual condition or performance of an asset to determine when maintenance should be performed.
Preventive Maintenance schedules maintenance according to predefined intervals, while Condition-Based Maintenance uses asset condition and performance data to determine when intervention is required.
No. Condition-Based Maintenance reacts to the current condition of an asset, while Predictive Maintenance uses data and analytics to forecast potential future failures.
No. Condition monitoring can be performed through manual inspections and measurements. However, IoT sensors enable continuous and automated monitoring.
Critical assets with measurable failure indicators and significant consequences when they fail are usually the best candidates.
Yes. By prioritising maintenance according to actual asset condition, organisations can reduce unnecessary interventions and potentially avoid costly failures.
Condition-Based Maintenance provides a practical way for organisations to move beyond fixed maintenance schedules and reactive repairs.
By monitoring the actual condition of assets, maintenance teams can identify deterioration earlier, prioritise resources and make better-informed decisions.
The approach does not require every asset to have sophisticated sensors or AI models. It can start with simple inspections and evolve towards connected monitoring, IoT and predictive analytics as the organisation's maintenance maturity increases.
The most effective strategy is to combine condition information with accurate asset data, maintenance history and structured work order management.
When integrated into an Enterprise Asset Management platform, Condition-Based Maintenance becomes part of a broader data-driven maintenance strategy—helping organisations improve reliability, reduce downtime and maximise asset value throughout the asset lifecycle.