Predictive Maintenance in Production and Manufacturing

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Below I have summarized the most common reasons why you should use predictive maintenance An example: You would like to offer data collection and analysis as an additional, paid digital service in order to

to be able to offer attractive full-service packages,
proactively offer the sale of spare parts and services,
to ensure greater availability for your customers,
to support your customers with operational and process know-how and
to differentiate yourself from the competition through added value.

A typical analysis process in predictive maintenance systems
Once a predictive maintenance system has been set up, the process is always similar: First, the data is collected in a central system, such as our adesso IoT Cloud. Possible sources are, as described above, machine data, the service system, process control systems or additional sensors. A retrofit solution is particularly attractive for digitizing existing machines cyprus consumer email list cost-effectively, as it already enables real-time monitoring.

This collected data must be analyzed accordingly so that the predictive maintenance system can recognize patterns and create a model from which the condition of the machine can be derived and maintenance recommendations can be made. A good and detailed model is crucial for the quality of the overall system.


Example Predictive Maintenance Flow in the context of your company
Many customers ask us how the predictive maintenance flow can be mapped in their company and whether other data sources are relevant in addition to sensor data. Other data sources offer immense added value and should definitely be considered. In your company, the data pipeline could look like this:


On the left side are the data sources. Here we often differentiate between static master data and historical or live data. The static master data comes from the surrounding systems such as SAP and contains, among other things, the asset descriptions and maintenance history of the machines. Valuable information can be hidden in these data sets. Perhaps the machine in question has had certain malfunctions several times in the past or has already been repaired frequently, so that future problems can also be derived from this data?

In addition, the live data from the condition monitoring of the machines must be considered.

It is important that we make all data analyzable - for this purpose, our experts often build a so-called data lake. All kinds of data are collected in this lake of data, often unstructured and therefore not put into a tight corset in advance. The data scientists then work on this data lake - with the help of frameworks and tools such as R or Python, among others.