Forecast the life of your cables
As experts in the field of connection technology, we have taken up the topic of predictive maintenance in order to develop solutions for the predictability of the service life of cables in industrial environments.
Especially in moving and force-guided industrial applications, such as operation in an energy supply chain, cables are subjected to strong bending and flexing loads. In robotic applications, torsional forces also act. Although the cables are specially designed for this type of load, they are still considered wear parts. Despite all this, high-quality cables can withstand several million motion cycles, but must be replaced at the latest then. If the cables are not replaced, the system can fail, resulting in unscheduled downtimes and therefore high costs.
Our solution for ETHERNET cables: LAPP Predictive Maintenance Box
LAPP presented the first concept a few months ago and there is now a usable prototype close to series production. The predictive maintenance box, or PMBx for short, is now small, compact, robust and flexible, and can also be integrated into existing systems. It reports promptly when an Ethernet cable is likely to fail. The box therefore helps prevent unexpected and expensive system failures and helps you to plan maintenance work.
The LAPP Predictive Maintenance Box continuously determines the power status of the cable and gives an alarm in time before the cable breaks.
What distinguishes LAPP's solution from all other concepts is that the box is simply connected to the data cable in series; special sensor elements in the cable or even a second device on the end of the cable are unnecessary. This makes it suitable for retrofitting in older systems. Users can connect the box to a gateway or cloud via WiFi using the IoT protocol MQTT. However, it is also possible to pick up the signal via a wired digital output or IO link. The box continuously calculates the LAPP predictive indicator and triggers an alarm if the transmission properties of a cable deteriorate and there is a risk of failure. The intervention threshold can be set by the customer.
With pilot customers on the way to series production
An intensive and early Customer Journey helps us to develop the prototypes marketable. The PMBx is currently being used by pilot customers from the medical technology, automotive and intralogistics industries. But also in our own logistics centre in Ludwigsburg (LC6), the data lines of the storage and retrieval machines have been monitored for half a year in the fully automated cable drum warehouse for their service life and quality.
Currently, the box is suitable for monitoring Ethernet lines; variants for power lines are planned.
If you are interested you can contact us under: iot-solutions(at)lappgroup.com
More detailed information: Predictive Maintenance
The core topic of Digital Transformation is the collection of machine data: Engines, valves and sensors automatically report their operating states. This data is collected and evaluated and analyzed with the help of self-learning algorithms such as AI or Machine Learning. The aim is to increase the efficiency of a plant or to generate a current status picture of it in order to detect imminent failures at an early stage. If these data or processes are managed via cloud systems, even worldwide access to the plant is possible. Distances become irrelevant - because everything is connected and digital. With their highly automated and time-critical production processes, mechanical engineering and the automotive industry in concrete are particularly interested in further developments in the smart theme field. In worst cases, even the failure of a single component can cause the entire manufacturing process to come to a standstill and thus cause enormous financial damage.
In order to reduce this risk, so-called condition monitoring systems offer the user the possibility to record and evaluate the performance status of any component. The plant operator is thus able to determine in good time when the failure of a component is imminent and can then initiate appropriate maintenance measures before the plant comes to an unscheduled standstill. This is particularly important for cost-intensive applications or safety-critical infrastructures. At present, a preventive approach is usually taken in such plants. This means that wear parts are replaced at predetermined intervals, regardless of their condition or remaining service life.
To avoid this, Predictive Maintenance can be used to forecast the failure of a system component. The approach of this predictive maintenance collects plant data during operation and evaluates it in a targeted manner. In order to optimise the costs and operational safety of the plant, the signal for the maintenance measure should neither be displayed too early (high costs) nor too late (failure of the plant).