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Predictive Maintenance

Predictive Maintenance in Industry 4.0

Avoid Breakdowns Thanks to Data and Artificial Intelligence

Breakdowns and malfunctions of mechanical equipment in industry can have serious consequences and should be avoided proactively. 


Thanks to predictive maintenance, the current condition of machines and plants is always transparent through permanent analysis of data. This helps to avoid unplanned breakdowns and to optimise the deployment of service staff in the field. Maintenance and service intervals as well as spare parts management can be planned much better. In addition, the performance of the machine can be improved and a higher productivity achieved.
 
Wind turbines, motor vehicles or turbines are ideal for predictive maintenance, because downtimes of wind turbines can be almost completely avoided.

Case Overview

Initial situation

Traditional maintenance methods carry a high risk. Only when errors or malfunctions have occurred a reactive analysis of the causing problem and measures to eliminate the malfunction are carried out. Machine failures cannot be proactively prevented in this way. Required service personnel and spare parts can only be ordered after the malfunction has occurred and the analysis has been completed. This can result in considerable downtimes.

Vision

By evaluating and analyzing existing and ascertainable machine and plant data and by using complex mathematical algorithms and machine learning methods, failures and malfunctions of industrial plants can be predicted and proactively avoided.

Solution

By using sensors, data is permanently collected, analysed and evaluated by an algorithm. Based on this data material and historical scenarios, malfunctions and possible failures can be predicted before they occur and by specifying the probability of occurrence. For example, engine speeds, noise or temperatures can be recorded and unusual vibrations or imbalances can be detected early on.

Advantages

Reduction of breakdown times
Optimized deployment of service personnel
Optimized spare parts management

Your Contact for this case

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Niels Pothmann
Expert for Advanced Analytics & Artificial Intelligence