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DXQequipment.analytics provides a deep insight into various process steps and involved equipment along the value chain. The software package aims at improving all aspects of the overall equipment effectiveness (system performance, production quality, equipment availability). In a first step, DXQequipment.analytics supports faster troubleshooting with root-cause-analysis visualizing critical situations, detected patterns, and exceeded thresholds. Secondly, an automated analysis is enabled by providing a drag-and-drop analytics builder to create own algorithms. With the deployment of such algorithms data can automatically be analyzed and a direct feedback to the machine in real-time can be realized. In future, the Advanced Analytics module will use historical data and machine learning to find the optimal parametrization of algorithms and to detect long-term trends and patterns. This AI application combines information technology with engineering expertise, identifies fault sources and determines the optimal times for scheduled maintenance. It finds correlations in the plant and adapts the algorithm using a self learning approach.

In combination with DXQplant.analytics self-learning algorithms will automatically be trained to identify quality issues. Alongside DXQequipment.maintenance further information on detected maintenance tasks is provided.

DXQequipment.analytics is based on Dürr’s expert knowledge and can be offered for various equipment types, e.g. application robots, ovens, PT / EC systems.


  • Real-time streaming analytics to ensure production quality
  • Self-learning quality anomaly detection
  • Easy-to-use frontends for data visualization and analytics model creation
  • Permanent acquisition and analytics of equipment data
  • Machine learning algorithms to evaluate the painting process and predict equipment failures


  • Faster troubleshooting to increase equipment availability
  • Increased first-run-rate
  • Decreased equipment downtimes
  • Optimized identification of root-causes