What is predictive maintenance?

While traditional reactive maintenance only intervenes after a defect has occurred and preventive maintenance involves setting regular maintenance intervals, predictive maintenance goes one step further: it is a modern approach to maintenance that aims to predict machine and plant failures before they actually occur. 
To this end, machine status data is continuously analysed using artificial intelligence (AI) and data analysis to predict the optimal time for maintenance. 
The aim is to avoid unplanned downtime, reduce costs and extend the service life of components. 

How does predictive maintenance work? 

Predictive maintenance is based on the collection, monitoring and evaluation of machine data in real time. 
Typical data sources are: 

• Sensors (e.g. temperature, vibration, pressure, oscillations, noise)
• Machine controls and IoT platforms
• Operating and performance data from ERP or MES systems 

This data is analysed using algorithms and machine learning models.
This allows deviations from the normal state to be detected at an early stage – for example, an unusual vibration pattern that indicates an impending bearing defect.
As a result, maintenance teams can intervene directly and in a targeted manner – even before an actual failure occurs. 

Predictive maintenance is condition-based – but goes even further

Predictive maintenance is often confused with condition-based maintenance (also known as CBM), as both are based on monitoring the condition of machines. 
In fact, predictive maintenance is a further development of condition-based maintenance: 

• In condition-based maintenance, maintenance is performed as soon as sensors detect a deviation from the normal state – i.e. reactively to the current state.
• In predictive maintenance, on the other hand, this condition data is evaluated using AI, machine learning and historical analyses to predict future changes and failures.

This transforms purely condition-based maintenance into a proactive, forward-looking system that enables intervention before a problem arises. 

Advantages of predictive maintenance

The use of predictive maintenance brings numerous advantages for companies: 

• Reduced downtime: Unplanned downtime is minimised.
• Cost savings: Maintenance is only carried out when it is really necessary – unlike with preventive maintenance.
• Longer service life of equipment: Early intervention prevents consequential damage.
• More efficient resource planning: Spare parts, personnel and maintenance windows can be optimally planned.
• Data-based decisions: Companies gain valuable insights into the condition of their machines. 

Areas of application for predictive maintenance 

Predictive maintenance is used in many industries, including: 

• Industry & production
• Energy and utilities
• Transport and rail
• Aerospace
• Mechanical engineering

Predictive maintenance is playing an increasingly important role in the railway industry in particular: sensors on freight cars, points or locomotives enable condition-based maintenance, which can significantly reduce operating and maintenance costs. 

Difference from preventive maintenance 

While preventive maintenance is based on fixed time intervals or usage cycles, predictive maintenance relies on data-driven condition monitoring. This means that maintenance measures are carried out according to actual need rather than according to a schedule. We have summarised the differences for you again in a clear and concise manner: