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Condition Monitoring

Condition Monitoring is a central IoT/Industry 4.0 entry scenario that helps you to learn more about your machines and products and enables you to reduce response times in the event of malfunctions

Condition Monitoring – a perfect entry point to IoT

Real-time Condition Monitoring of machines, systems or products is an ideal start into IoT. On the one hand, capturing, collecting and visualizing information from devices is a basic technological prerequisite for many other IoT use cases. On the other hand, handling, analyzing and understanding data in a running Condition Monitoring system provides an ideal opportunity for growing into the topic of IoT.

A key added value of Condition Monitoring is the reduction of response time: If you are informed in real time about malfunctions and downtime of machines or products, regardless of where they are located, you can react immediately and fix the problem.

In addition, you will also learn to better understand correlations by analyzing and interpreting the data:

- If you operate a heterogeneous machine park with assets from different manufacturers, you will be able to catch the interaction and interdependencies more clearly.

- But even if you monitor your own products – which no one knows better than you – at the customer's site, you will gain valuable insights into their performance over time, better under specific environmental conditions etc. This information you can use to improve the product itself or to offer better customer service.

Last but not least you will give your digitization agenda a fundamental boost: Whatever you do in future - collecting, analyzing and understanding data will be a central part of it. And this is exactly what you’re doing in context of Condition Monitoring - on a large scale and directly linked to added value for your business.


The data basis for condition monitoring

The data basis for Condition Monitoring can come from different sources: for example, machine data read out from controllers, feedback from signal lights or via connected sensor solutions.

The resulting data volumes, the raw data, are usually very large - Big Data - and are in most cases not directly needed for a specific application. If you measure the temperature every second, for instance, this information is probably not relevant for a Condition Monitoring scenario - but rather at which instant of time a temperature change occurred and how big it was. When data is put into context in this way, it is referred to as smart data. In terms of data volumes, smart data is usually much smaller than raw data.

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