The trade-off between corrective and preventive maintenance on assets is a difficult one: How do you ensure that maintenance is not carried out too early or too late, with possible additional costs or damage as a result? Predictive Maintenance can offer a solution here. In this white paper we would like to take you through the techniques behind predictive maintenance and the conditions for implementing it.
Predictive maintenance is predicting the right time for maintenance based on data. The techniques behind predictive maintenance help determine the condition of assets. Data from the maintenance process plays a major role in this. Based on this data, it is possible to predict when products, devices or machines need maintenance. There are several advantages to this: the availability of these assets is increased, maintenance costs decrease and the use of asset data provides more insight into the production process itself.
The asset data that forms the basis of predictive maintenance is collected through the use of sensors. These sensors can be set according to your insight: at which values assets function well, at which values action must be taken and at which values defects occur. The sensors register the current values and the user receives a signal when those values are approached. In this way it becomes clear when an asset needs to be maintained or repaired.
Predictive maintenance can be used in many sectors and can in principle be applied to anything that can be set in motion electrically or mechanically. This makes it an interesting way of maintenance for, among other things, (rail) infrastructure, process engineering environments, and for regular building maintenance. But predictive maintenance can also be a solution for ICT-related systems.
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