IMPROVE provides an innovative self-learning condition monitoring solution that prevents producers from unexpected breakdowns or product degradation. The outcome is translated into different software options, ready for industrial use.
Central characteristics of our data-driven condition monitoring:
- Detecting and localising anomalies from learned normal behavior models
- Providing different types of models for the anomaly detection and localisation
- Easy implementation of additional types of models and monitoring algorithms as data acquisition for learning is flexible
- Providing information about signals, last anomalies and also a live visualisation of the model
- Allowing customers to forecast problems on the machine that could lead to production stoppages
- Available as an additional after-sales service that provides regular reports of the machine ef¬ficiency throughout the lifetime
- Carrying out short-time forecast analysis to identify wrong machine settings after a product changeover or problems related to raw material changes
IMPROVE’s condition monitoring software provides an allround solution that could lead to a great change in the service procedures of automatic machines and will significantly improve the production process.
For more information, please contact our coordinator Prof. Dr Oliver Niggemann.
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Alexander von Birgelen (HS-OWL) in the interview on the IMPROVE approach