The reliability of machinery is a critical factor in modern industry. Every unplanned stoppage entails economic losses, production delays and safety risks. Faced with this challenge, organisations seek strategies that allow them to anticipate failures. One of the most effective is condition-based maintenance (CBM).
This approach relies on monitoring the real state of equipment to decide the right moment to intervene. It is not about routine servicing or waiting for a breakdown to occur, but about acting when data show that a machine is deteriorating.
What is Condition Based Maintenance?
Condition-based maintenance is a proactive strategy that uses sensors, data-acquisition systems and analysis tools to understand in real time how an asset behaves. When measurements detect deviations from normal values, the intervention is scheduled before a functional failure occurs.
Standard ISO 17359 sets out the guidelines for condition monitoring of industrial equipment and describes which parameters are advisable to measure (vibration, temperature, pressure, lubrication, etc.).
In practice, this means companies do not rely on predefined maintenance calendars, but on the actual condition of their equipment. This optimises resources and reduces unnecessary stoppages.
Key elements of CBM
For condition-based maintenance to work properly, you need:
- Sensors and real-time data acquisition: measuring parameters such as vibration, temperature, pressure, noise levels or oil quality.
- Advanced analytics platform: artificial intelligence tools and machine-learning algorithms to identify anomalous patterns.
- Trend models: to predict when the equipment will reach an unacceptable level of degradation.
- Action thresholds: predefined values indicating when maintenance should be scheduled.
- Integration with asset-management systems (CMMS/GMAO): to plan resources, spares and downtime efficiently.
Advantages of Condition Based Maintenance
Adopting this strategy brings significant benefits:
- Higher reliability: by detecting failures before they occur, unplanned stoppages are reduced.
- Cost optimisation: unnecessary inspections are eliminated and premature component replacement decreases.
- Extended asset life: intervening at the right moment prevents greater damage and preserves machinery.
- Better safety: identifying anomalies before they lead to an accident reduces risks for workers.
- Lower environmental impact: keeping equipment in optimal condition consumes less energy and generates less waste.
Compared with traditional preventive maintenance, CBM provides a dynamic view of plant health, which translates into more precise and efficient management.
How to implement CBM (step by step)
Adopting condition-based maintenance requires structured planning:
- Selection of critical assets: identify which equipment justifies continuous monitoring due to its importance to the process.
- Definition of control parameters: decide which variables will be measured (e.g., vibration on a motor, pressure on a pump, temperature on a bearing).
- Installation of sensors and acquisition systems: ensure measurements are reliable and continuous.
- Data processing: use analytic tools that can distinguish normal variation from signs of degradation.
- Planned corrective actions: schedule the intervention before the failure, optimising downtime.
- Continuous improvement: review the system, add new sensors or adjust thresholds based on accumulated experience.
Success depends on both technology and people. Maintenance teams must be trained in data analysis and the use of digital tools.
Functions of a CBM system
A condition-based maintenance system should be able to:
- Detect worn components or those at risk of breaking.
- Identify the precise moment when a functional failure will occur.
- Analyse machine-behaviour trends to produce predictive reports.
- Reduce human and mechanical maintenance costs by intervening only where necessary.
The most common monitoring techniques include vibration analysis, infrared thermography, lubricant analysis and tracking of electrical variables. Depending on the type of asset, some techniques will be more suitable than others.
Condition based maintenance vs predictive maintenance
Condition-based maintenance and predictive maintenance are often confused, but they are not the same:
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CBM: focuses on the asset’s current state, taking decisions from thresholds and values measured in real time.
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Predictive maintenance: uses statistical and historical models and advanced algorithms to forecast future failures and optimise long-term planning (see article Industry 4.0 predictive maintenance plan).
In practice, CBM is an intermediate step towards advanced predictive maintenance. Many companies start by installing CBM systems and, over time, evolve towards more complex predictive analytics strategies.
This evolution makes full sense when seen in practice: explore our examples of predictive maintenance and how they complement condition-based maintenance.
Challenges and limitations
Although condition-based maintenance is highly effective, it presents certain challenges:
- High initial investment: installing sensors and analysis systems can be expensive. Mitigation: opt for cloud platforms under a SaaS (Software as a Service) model to avoid large on-premises server investments. Subscription payments reduce the initial outlay and make it easier to scale according to the number of monitored assets. Benefit: converts a capital expense (CAPEX) into an operating expense (OPEX).
- Managing large data volumes: storage and processing infrastructure is required. Mitigation: implement platforms that integrate cloud storage, real-time processing and advanced analytics. By centralising information, silos are avoided and access to unified dashboards is facilitated. Benefit: enables large-scale machine-learning and predictive analysis without overloading the company’s internal infrastructure.
- Risk of false alarms: poor sensor calibration can trigger unnecessary interventions. Mitigation: use AI-based detection algorithms that learn from each asset’s historical behaviour. Also, establish a data-validation process with maintenance technicians before issuing critical alarms. Benefit: reduces unnecessary interventions and increases staff confidence in the system.
- Need for qualified personnel: interpreting data requires training and experience. Mitigation: choose platforms with intuitive graphical interfaces, integrated with CMMS/GMAO systems, offering understandable alerts (traffic-lights, KPIs, automatic reports). Complement with training programmes in data analysis and Maintenance 4.0. Benefit: democratises system use so that not only expert analysts can interpret results.
These factors should be considered at the planning stage to ensure return on investment.
Conclusion: the future of condition-based maintenance
Condition-based maintenance offers industry a balance between preventive and predictive approaches. It allows intervention at the right moment, avoiding both the cost of unnecessary maintenance and the risk of unexpected failures.
However, its implementation is not without challenges. The good news is that there are now cloud, multiprotocol and multi-vendor platforms capable of mitigating many of these limitations.
Ultimately, CBM is not only a technological matter, but an organisational one: it requires management commitment, investment in training and a clear asset-management strategy. When supported by advanced digital platforms, it becomes more accessible, scalable and aligned with the principles of Industry 4.0.
In this way, condition-based maintenance not only improves current plant reliability, but also forms the basis for evolving towards predictive and prescriptive maintenance, where artificial intelligence and human-machine collaboration will set the pace in the future.
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