Gantner Instruments Test & Measurement GmbH
Heidelberger Landstr. 74
64297 Darmstadt (Deutschland)
Tel: +49 6151 95136-0
Fax: +49 6151 95136-26

GI.connectivity - Offene und flexible Schnittstellen auf allen Ebenen

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Asset monitoring engineers in the energy and building sector require more than a hardware kit to collect data of their analog or digital sensors. They are looking for a complete DAQ solution which offers seamless integration and connectivity to their current operation and maintenance framework in order to generate directly relevant information based on their acquired data streams. This data is needed for AI, machine learning and predictive maintenance strategies.

GI.connectivity provides safe and reliable data exchange and interoperability through various read/write interfaces at the edge, desktop and cloud level. GI.connectivity will transform your Q.series X system into the most open and flexible data acquisition and streaming platform available on the market.

With the smart edge devices from Gantner Instruments you can select any type of analytic function you may need for analyzing your measurement data, both in the time and frequency domain. Typical measurement data for asset monitoring includes acceleration, strain, temperature or any industrial protocol.

Key Features:

- Fieldbus interfaces, including CAN, EtherCAT and PROFINET
- Custom controller plugins for customer-specific communication protocols
- Drivers & interfaces for LabVIEW, MATLAB, Python and many more
- IIoT protocols, such as OPC UA, DDS, and MQTT
- Edge applications for AI, machine learning and data analytics
- Modern APIs for device configuration and data stream access

Different options for data transmission from a smart edge device to a local server or cloud based system can be selected (OPC UA, MQTT, Modbus, …) and integrated to any SCADA platform, cloud platform or Gantner’s own cloud service. It is also easy to ingrate the complete system configuration and data streaming into existing software environments like e.g LabView VI’s. Data scientists can read/write the data via APIs with their platform of choice (Python, Matlab and many more).

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