Predictive Maintenance for complex Pump Systems & Plants with Federated Learning
Read how a pump manufacturer successfully captured a data-sensitive customer segment.
As a manufacturer of pumps, you would like to win a new customer segment for your cloud-based predictive maintenance solution. The additional offering is intended for operators of complex pump systems and plants who do not want to share their process data. At the same time, the customer target group expects to be able to integrate the predictive maintenance solution into their own existing systems.
Learn in this case study how a pump manufacturer successfully integrated a new customer segment with these demands into its existing solution through Federated Learning.
The Federated Learning Case Study for Mechanical Engineering Specialists in Pumps and Compressors.
Cavitation operation should be avoided in the future with your predictive maintenance solution. To enable more complex cavitation predictions and increase data availability, you want to integrate additional customers with more complex pump systems. They insist on local data processing on their shop floor while your existing customers continue to transfer their data directly to your cloud. Learn how a pump manufacturer combines decentralized and centralized machine learning to achieve better cavitation predictions for all customers.
Your Key Benefits
- Integrate an additional offering for more complex pump systems into your existing solution
- Improve forecasting for all customers through a hybrid approach
- Provide real-time custom predictions for complex assets
- Enable integration of custom predictions into existing customer systems
- Integrate data-sensitive customers into your existing solution
- Pumps learn from each other - without learning about each other
Read our case study to learn more about how you can leverage these benefits to strengthen your competitive position.
Learn how a pump and compressor manufacturer uses Katulu Federated Learning to successfully win a new customer segment with their existing predictive maintenance solution.
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