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Data Insights - Event-based, AI-controlled switching point for pressure sensors

Data Insights - Event-based, AI-controlled switching point for pressure sensors

Our AI solution enables precise, dynamic switching activation through intelligent detection of trigger events - flexibly adaptable to changing conditions and requirements.

Cases

What we do

In certain applications, it is not appropriate to link the triggering of a sensor switching output to a fixed value, but an event-based trigger is required for the switching activation. Our AI solution revolutionizes this process by using machine learning and data analysis to trigger the switching output quickly, precisely and dynamically by recognizing a trigger behavior - especially with many different, changing conditions and requirements.

01

The advantages

  • New applications: Adaptive switching point determination enables the use of sensor-controlled systems in new areas
  • Precision: continuous optimization based on comprehensive data patterns minimizes errors and increases system performance.
  • Real-time adaptation: Flexible response to changes in process parameters and ambient conditions.
  • Scalability: Can be used in a wide range of sectors, from industrial automation to parcel handling and medical technology.

02

The technology behind it

Our solution combines state-of-the-art AI technologies and data-driven approaches:

  • Neural networks: Analyze and learn from large amounts of data to generate precise triggering of the switching output.
  • Predictive analytics: Use of historical and real-time data for proactive and adaptive optimizations.
  • Automated integration: Direct connection to existing sensor/actuator systems and control software for seamless implementation.
Data Insights

An example from practice

A manufacturer of parcel handling systems in leading parcel logistics centers wants to eject a parcel that is too heavy from the tube lifters before it breaks off. This prevents damage to the tube lifter. Due to the large number of different parcel shapes, surfaces and packaging materials, it is not possible to preset a specific vacuum value. Tearing off must be detected in advance by the vacuum behavior at the holding point and an ejection process must be initiated.

  • The AI continuously analyzes data streams from the sensor network and detects patterns and deviations.
  • The trigger behavior can be continuously improved through user feedback.
  • Extremely short response times can be realized through local data processing in the sensor.

The result: By detecting the vacuum behavior when the tube lifter is overloaded, a trigger is derived which opens a release valve by means of a sensor switching output so that the package is ejected before it tears off. This considerably extends the service life of the tube lifter and massively reduces operating costs, which is a significant competitive advantage, especially in large logistics centers.

Conclusion

With our AI-supported solution for data insights, you can optimize the performance and service life of your systems, minimize downtimes and maximize process reliability. Use the power of AI to automate data-based decisions and shape the future of your technologies.

Curious about what we can do for you?

We look forward to hearing from you!

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