Forum Industrial IoT & Maintenance
October 30 in the Darmstadtium!
Let's keep moving on!
Identification in a Challenging Environment
In highly automated productions, identification systems must work with absolute precision. At the same time, the robustness of the hardware used is decisive for sustainable solutions.
Radio-based detection technologies also work in harsh environments with metal, high temperatures, chemicals or poor visibility due to dust and dirt.
RFID & Co provide insights into previously invisible processes and support the development of comprehensive information systems.
Localisation of Assets and Employees
Wireless IoT technologies are used to locate objects and people. Depending on the requirements for range and localisation accuracy, passive or active UHF RFID systems, BLE or WLAN can be used.
RTLS enable position determination in real time and thus ensure maximum transparency in productions as well as in upstream and downstream logistics processes.
Locating people increases security in highly automated or exposed production environments.
AI in the Industry 4.0
AI systems unfold the potential to generate knowledge and to continuously optimise the condition of machines and complete production lines in live operation based on data and algorithms.
Possible errors or malfunctions can be reliably predicted. The data basis for AI systems in production processes is created with the support of wireless IoT technologies in combination with sensors.
Wireless IoT in the Industrial Production
Modular production lines react flexibly to dynamic markets. M2M communication via IWLAN, RFID or 5G campus network on the basis of OPC UA realise self-controlling productions. Wireless IoT technologies together with the Digital Twin support factory planning.
Embedded RFID tags enable digital product lifecycle management. Sensors plus wireless interfaces create the database for predictive maintenance applications.
AI applications in combination with wireless technologies can provide added value for machine safety and predictive maintenance processes.