A machine that unexpectedly breaks down can quickly cause problems in the production process. Operations come to a standstill, delivery schedules may come under pressure and repairs take time. With predictive maintenance, abnormalities in machine components can be detected at an earlier stage. This creates more opportunities to carry out maintenance at a suitable time, before a failure disrupts production. But how exactly does this approach to maintenance work?
What is predictive maintenance?
Predictive maintenance is a maintenance method that uses measurement data to assess when a machine component is likely to require maintenance. Instead of relying solely on fixed maintenance intervals, the actual condition of components is taken into account.
To achieve this, sensors collect data on factors such as vibrations, temperature or other changes during operation. By analysing this data over time, abnormalities that indicate wear or a developing defect can become visible. This allows maintenance to be scheduled earlier, without necessarily having to replace every component immediately.
The difference between preventive and predictive maintenance
With preventive maintenance, machines are inspected at fixed intervals. Examples include lubricating a linear guide after a certain number of operating hours or replacing components according to a maintenance schedule. This helps reduce failures, but does not always take actual wear into account.
With predictive maintenance, the condition of the component is the starting point. A linear guide that is still functioning properly does not automatically need to be replaced simply because a scheduled interval has been reached. Conversely, an abnormality may be a reason to carry out maintenance earlier.
The main difference therefore lies in when maintenance takes place. By using measurement data, maintenance can be planned more precisely and unnecessary work can be reduced.
How does predictive maintenance help prevent unplanned machine downtime?
Many technical problems develop gradually. A bearing may start vibrating more, a linear guide may show signs of wear and a drive system may become warmer than usual. Such changes are not always immediately noticeable during the production process.
With specialised sensor technology, these signals can be monitored. The collected data is compared with previous measurements or known operating patterns. When a component behaves abnormally, this may indicate that an inspection is needed.
This provides more time to prepare for maintenance. Replacement parts can be ordered and work can be scheduled during a planned production shutdown. This reduces the risk of a machine breaking down unexpectedly.
However, predictive maintenance cannot prevent every failure. Sudden defects without measurable warning signs remain possible. The quality of the measurements and the correct interpretation of the data also determine how reliable the predictions are.
Predictive maintenance for linear guides and ball screws
Within linear motion systems, components are exposed to movement, friction and varying loads on a daily basis. This affects the wear of components such as linear guides and ball screws.
Particularly in machines that perform a large number of movements, insight into the condition of these components is valuable for the maintenance department. A change in motion behaviour may indicate insufficient lubrication, increasing wear or damage.
With suitable sensor technology, such changes can be monitored without relying solely on manual inspections. Correct installation, lubrication and loading of the components naturally remain important.
From scheduled to predictive maintenance with the right systems
At LM Systems, we can also make use of predictive maintenance. One example is THK OMNIedge, a system that uses sensor technology to monitor the condition of machine components.
The technology is available for linear guides, ball screws and rotating components, such as motors and bearing systems. For linear components, measurement data is analysed to provide insight into factors such as lubrication and possible damage. When abnormalities are detected, the system issues a notification indicating that inspection or maintenance may be required.
This makes the condition of components more visible. It helps ensure that maintenance decisions are based not only on fixed intervals, but also on data collected during operation.
Getting started with predictive maintenance
Not every machine needs to be fully equipped with sensors straight away. Identifying which components would have the greatest impact on the production process if they failed can also be a first step.
Next, you can investigate which changes are measurable and which sensor technology is suitable. By collecting measurement data over a longer period, you gain a better understanding of the machine’s normal operating behaviour. This makes abnormalities easier to recognise.
This data remains a tool for maintenance planning. However, technical knowledge and regular inspections are still necessary to interpret the signals correctly.
Unfortunately, completely eliminating unplanned machine downtime is not possible. However, with a good maintenance plan and insight into the condition of components, many problems can be detected earlier. A reliable motion system also starts with selecting suitable components. Our range of linear guides includes various designs for a wide range of applications. We are happy to help assess the technical requirements and select the right products.