What issue did the customer face?
The customer was struggling with a recurring problem in the operation of its robot systems: the hose package guided on the robot - consisting of cables and lines - was exposed to mechanical loads as it was often carried along in an uncontrolled movement during robot operation. This often caused the cable to dangle into the work area, became entangled or suffer damage by constant bending and tension. This not only led to increased wear and cable failure, but also to unplanned downtime and additional maintenance work. In order to guarantee the functionality of the hose package, continuous condition monitoring should be carried out to detect potential wear at an early stage and thus minimize downtimes.
What does the solutions look like?
A hosepack retraction unit has been integrated into the system, which is equipped with an infrared sensor with an analog output or an ultrasonic sensor with a “time to flight” output. These sensors are used to measure the distance traveled. The recorded sensor data is processed and analyzed in real time. This task is performed by a Raspberry Pi Compute Module 4 (CM4), which is mounted on a custom-designed carrier board developed by esd electronics. This setup reads the data from the sensors, pre-processes it and calculates the wear using an algorithm. The collected and processed information is then transferred to a cloud service. The cloud connection enables central data storage, further evaluations, visualizations and integration into a higher-level maintenance or monitoring system.
How does this solution benefit the customer?
By implementing the monitoring system based on the Raspberry Pi CM4, the customer benefits from a significant increase in system availability and a reduction in unplanned downtime. A key component is the customized carrier board developed by esd electronics, which enables both the connection and evaluation of the sensors and acting as a central interface for monitoring the condition of the hosepack.
The continuous recording and analysis of condition data enables early identification of wear or anomalies in the hosepack before a failure occurs. The IoT connection allows this data to be seamlessly forwarded to a cloud-based infrastructure, providing the customer with centralized access to all relevant operating and diagnostic data from any location. This not only facilitates maintenance planning, but also forms the basis for predictive maintenance, which reduces maintenance costs and allows resources to be deployed in a more targeted manner.
