Predictive maintenance

Predictive maintenance is a proactive approach to maintenance that utilises advanced data analysis techniques and technology to predict when equipment or machinery is likely to fail. By monitoring the condition of assets in real-time and analysing historical data, organisations can identify signs of potential issues and take preventive action before major breakdowns occur. This approach can help to optimise maintenance schedules, reduce downtime, and increase operational efficiency.

Traditionally, maintenance practices have been based on two approaches: reactive and preventive maintenance. Reactive maintenance involves fixing equipment after it breaks down, leading to costly repairs, unplanned downtime, and potential safety hazards. On the other hand, preventive maintenance relies on scheduled maintenance activities performed at regular intervals, regardless of the actual condition of the equipment. While preventive maintenance reduces the risk of sudden failures, it can be inefficient and costly, as it may result in unnecessary maintenance tasks and the replacement of components that still have a useful life.

Predictive maintenance involves continuous monitoring of equipment using sensors and collecting data on various parameters such as temperature, vibration, pressure, and fluid levels. This real-time data is then analysed using sophisticated algorithms and predictive models to identify patterns, anomalies, and early warning signs of potential failures.

One of the key advantages of predictive maintenance is the ability to detect and address issues at an early stage. By identifying subtle changes in equipment behaviour or performance, maintenance teams can intervene before a failure occurs. This approach minimises the risk of unexpected breakdowns, reduces the need for emergency repairs, and prevents costly production interruptions. Furthermore, predictive maintenance allows organisations to optimise maintenance schedules, ensuring that maintenance tasks are performed when they are actually needed, rather than on a fixed calendar-based schedule.

Implementing a predictive maintenance program requires the integration of various technologies and systems. Sensors can be deployed to collect data from equipment and transmit it to a central data storage and analysis platform. Advanced analytics tools and machine learning algorithms process the data, identifying patterns and correlations that can predict future failures. The insights gained from these analyses are then used to generate actionable maintenance recommendations.

Predictive maintenance can be used effectively across various industries. For example, in manufacturing plants, predictive maintenance can help reduce equipment downtime. In the energy sector, predictive maintenance can optimise the performance of power plants and renewable energy systems, ensuring maximum efficiency and uptime.

Using predictive maintenance is a great way to protect expensive equipment against unexpected failure. To find out which predictive maintenance systems are right for your business, please contact our team today.

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Monitran Product Range



Ideal for use with portable and on-line vibration analysers.

Monitran 5032

Monitoring Systems

Monitoring systems that can be used to measure vibration.



Switch Enclosures and Connection Enclosures by Monitran.

linear variable differential transformer


LVDT'S are a popular tansducer in industrial applications.

eddy current probes and drivers


The latest precision-wound copper coil Proximity Sensors.



Allowing the user access to a vibration sensor for machinery.

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Established in 1986, Monitran remains a privately owned company, based near High Wycombe 35 miles west of London.

We manufacture in the United Kingdom and source the majority of our materials and components from selected quality-managed local and national suppliers.

Call Us: +44 (0)1494 816569

Monitor House
33 Hazlemere Road
HP10 8AD