Predictive Maintenance Optimizes Maintenance

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

• replacing defective parts at an early stage,
• reliably determining the remaining service life of parts,
• avoiding unplanned production downtimes, and
• thus saving costs and reliably serving customers?

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.

Starting point A Typical Predictive Maintenance Challenge

A large system is equipped with a variety of sensors that record measurements at short intervals. These measurements are expected to provide early information if an unusual condition occurs in the system. Such fault conditions should be detected and addressed early. It is important to note that sensor values often depend strongly on external conditions and on each other. What maintenance work is necessary and when should it ideally be carried out?

What to do Solution: Multivariate Monitoring of Sensors

Multivariate monitoring of sensors supports the detection of fault conditions. This means, in particular, that not only the behavior and progression of individual sensors are decisive, but also how different sensors behave in relation to each other. In the event of unexpected sensor behavior, for example when certain thresholds are exceeded, warning signals can be issued 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 advantageous in the case of complex interactions between sensors.

Through these predictive analyses, maintenance work can be planned early. A user dashboard enables the display of signals as well as the tracing of the cause or location of the fault. By providing feedback on the reported signals, users enable the underlying artificial intelligence methods to continuously learn and become even more reliable over time in recognizing and reporting unusual behavior in the system.

If faults occur frequently, machine learning methods can help to identify the patterns and correlations that lead to the respective faults. Not infrequently, such correlations become visible for the first time in this way and can be taken into account in the future. This means that action only needs to be taken when the wear and tear of components or machine parts actually becomes acute and not, for example, just because a certain period of time has passed since the last replacement. This saves time and effort.

Further information Video Material on the Topic of 'Predictive Maintenance'

A deep dive into the data science use case “Planning Maintenance Activities with Data Analytics” using the example of wind turbine monitoring is provided by the video "Planning Maintenance Activities with Data Analytics".

Blog Articles on the Topic of ‘Predictive Maintenance’