Predictive Maintenance - Avoidance of Unplanned Production Downtimes

You are responsible for the maintenance of several complex machines or systems and are interested in

  • replacing defective parts early,
  • reliably determining the remaining service life of parts,
  • avoiding unplanned production downtimes, as well as
  • saving costs and being able to serve customers reliably?

Then Predictive Maintenance could be the tool of choice to achieve your goals. Or simply find out more about this exciting data science use case and its applications on this page.


A typical “predictive maintenance” question and solution

A large system is equipped with a large number of sensors that record measured values at short intervals. It is hoped that these measured values will provide early information if an unusual condition prevails in the system. Such error states should be recognized and addressed at an early stage. It should be noted that the values of the sensors are often heavily dependent on external conditions and each other. What maintenance work is necessary and when should it be carried out?

Multivariate monitoring of sensors supports the detection of error states. This means that not only the behavior and progression of individual sensors are decisive, but also how different sensors relate to one another.

If the sensors behave unexpectedly - for example, if certain thresholds are exceeded - warning signals can be returned automatically. A major advantage is that such critical thresholds can be determined using data analytics and do not have to be specified by experts. This is particularly beneficial in the case of complex interactions between sensors.

These predictive analyses allow maintenance work to be planned at an early stage. A user dashboard enables the display of signals and the tracing back of the error cause or error position. By providing feedback on the reported signals, the underlying artificial intelligence processes can be successively learned and, in the long run, can recognize and report unusual behavior in the system even more reliably.

If errors occur frequently, machine learning methods can help recognize the patterns and connections that lead to the respective errors. Such connections can become visible in the first place and can be taken into account in the future. It is sufficient to take action when the wear and tear of components or machine parts is really acute and not when a certain specified period of time has passed since the last replacement. This saves time and effort.

Blog articles on “predictive maintenance”

Video material

This video provides a deep dive into the data science use case “Planning Maintenance Activities with Data Analytics” using the example of monitoring wind turbines:

Get in touch

Are you interested in one of our use cases and would like to discuss it with Tina Geisberger? Contact her to find out how we can assist you.

Bild Tina Geisberger

Tina Geisberger

Senior Account Manager - Tina's passion and expertise are use cases and she is eager to work with you to specify which of our use cases fit your situation. Through her years of professional experience, she knows how important it is to listen carefully to find out how our predictive analytics solutions can simplify your day-to-day work. She is extremely solution-oriented and welcomes any challenge - what does yours look like?


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