IoT application using in-house hardware in electronics manufacturing | Topics
We have expanded our in-house electronics manufacturing to include a new Internet of Things application using our own devices, thereby further broadening our range of demonstration solutions. This enables our customers and partners to monitor their own production, identify potential failures at an early stage and take timely action.
In the age of the Internet of Things, production processes are interconnected via intelligent individual components. Our comprehensive IoT package enables the retrofitting of your existing machinery, bringing it up to Industry 4.0 standards. This allows maintenance intervals to be optimised through predictive maintenance and condition monitoring, ultimately leading to cost savings. Consequently, the production manager has an overview of all key processes, is alerted to faults (e.g. a drop in temperature or loss of humidity) via an early-warning system, and is able to take prompt action. In addition, machine signals and power consumption can be monitored. The latter, in particular, enables peak shaving and thus also a reduction in costs.
For this application, a flexible, intelligent network is established using, amongst other things, sensor beacons and asset tracking beacons; this network operates with minimal data volumes and transmits this data to the gateway. What makes this solution unique is the use of our own hardware. With the help of our in-house sysWORXX CTR-100 and CTR-700 controllers, which act as controllers with gateway functionality, the data from the beacons can be collected, processed and transmitted. The data is visualised using Node-RED.
We can also equip your production facilities with our innovative IoT hardware – without the hassle of complex cabling. This enables you too to save costs and resources through predictive maintenance.
We can also equip your production facilities with our innovative IoT hardware – developed and manufactured by SYS TEC electronic in Germany. This enables you to save costs and resources through predictive maintenance.
