Sensor systems
The Industry 4.0 case
Fraunhofer IOSB-INA has developed a new type of mobile sensor system. What looks like luggage is actually a mobile 'Industry 4.0 analysis case' for machines and systems.
The almost 80 cm long and 50 cm wide 'INAsense' production data acquisition system: various sensor types collect the data - the system sends it to the cloud for processing without additional hardware.
© Fraunhofer IOSB-INAThe mobile sensor case 'INAsense' from Fraunhofer IOSB-INA is designed to make it possible to temporarily record data from existing processes as well as data from additional sensors and transmit it to a database via mobile radio. The basic idea behind it: The entrepreneur does not need to invest in their own hardware or sensor technology specialists.
Dr. Holger Flatt: "A major advantage is that the company does not need to have its own sensor hardware or Industry 4.0 expertise."
© Fraunhofer IOSB-INAWith the new sensor system, the developers want to make it easier for their partners, especially SMEs, to gain more transparency, control, predictability, safety and flexibility in their own production in the future. Dr. Holger Flatt, head of the "Intelligent Sensor Systems" research group at Fraunhofer, sums up the incentive for this development project: "With the mobile sensor system, we want to collect data on site, create transparency and identify potential or support the detection of problems."
The technological bandwidth
The sensor system offers a wide technical range for obtaining this data: Infrared, vibration, pressure, distance, temperature, humidity and system performance measurement. According to the developers, the system also detects various other physical properties of the system. A programmable logic controller (PLC), an internal computer and a gateway as well as an LTE router for transmitting the sensor data to a secure cloud are also installed.
The production data acquisition system should prove useful in a wide variety of scenarios in the future. For example, in quality assurance to create transparency in the process or predict upcoming maintenance. Machines can also be operated more efficiently and effectively based on the findings from the production data.
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