LAPP futureLab discussces Predictive Maintenance initiative for Ethernet industrial cable
Knowing when the cable will break
Replacing parts before they fail to avoid unplanned downtime - that's what predictive maintenance promises. But how to determine the aging of a cable and predict when it will fail? LAPP has developed a solution for Ethernet cables that doesn’t require any changes to the cable.
The LAPP Predictive Maintenance box is being further
developed and refined in the LAPP Test Centre
Predictive maintenance is one of the foremost and most promising benefits of digitalization in factories. Instead of replacing parts only when they have already failed and the machine has stopped working (reactive maintenance) or replacing still functional parts (preventive maintenance), predictive maintenance is based on sensor data that allows conclusions to be drawn about the actual aging of the part. The question of how to implement predictive maintenance also concerns connection systems. Even though cables usually last for many years, failure cannot be completely ruled out, especially in demanding applications. The importance of cabling should not be underestimated: a part that only costs a few dollars can paralyze an entire production at a high cost. "That's why we want to offer a solution that will sound the alarm before a cable fails," says Guido Ege, Head of Product Development and Management at LAPP.
Focus on Ethernet cables
Ethernet cables, especially when laid in energy chains, are more prone to failure than current-carrying cables due to their complex structure and the necessary high-frequency characteristics. For instance, broken shielding leads to increased EMC interference. If strands of wire break, the attenuation increases and the data rate drops. If a strand breaks, communication fails completely. Guido Ege's team has therefore concentrated on Ethernet cables and developed a predictive maintenance solution for them. The aim was to be able to predict the remaining lifetime of a cable and plan replacement in a way that would guarantee minimum disruption of machine operation. For this purpose the transmission characteristics of data cables are monitored, with changes in those characteristics are used to calculate the expected service life. "We want to help make factories smart, and predictive maintenance is a key issue here," says Ege.
Function of the Predictive Maintenance System for
data cables from LAPP
Solution without “sacrificial wires”
It was imperative to develop a measuring principle that works without changing the cable, i.e. without additional “sacrificial wires” in the cable. Users do not want additional wires because they require additional installation work. The solution should be based only on a protocol and a special algorithm so that standard Ethernet cables and standard connectors such as RJ45 or M12 can be used. The installer connects the cables as usual and does not have to connect any additional sacrificial wires. This approach has the additional advantage that existing systems can be retrofitted.
The ETHERLINE® TORSION Cat.7 is a high-performance
cable for industrial Ethernet.
The measurement takes place in the PMBX (Predictive Monitoring Box). The PMBX has two Ethernet ports and is inserted at the beginning of the Ethernet cable to be monitored. The data packets are transmitted transparently from one Ethernet port to the other, almost without delay. For a connected PLC, the PMBX is not visible and has no influence on data transmission. Therefore it is also suitable for existing systems without having to make any changes to the PLC software.
The Predictive Monitoring Maintenance System Box
from LAPP is inserted into the cable to be monitored
and is not visible to a connected PLC
LAPP Predictive Indicator
The LAPP Predictive Indicator calculates its failure prognosis on up to four transmission-relevant parameters. Plausibility checks are also possible by measuring several variables. This minimizes misinterpretations of measured values. The predictive maintenance system uses a deep learning approach. For LAPP drag chain cables, millions of measured values have been collected in our in-house test center and then analyzed using mathematical algorithms. During the development process, LAPP analyses the data locally on a PC but this can also be done later in the cloud, depending on customer requirements. The more data that is available, the more accurate the prediction: the system is self-learning. After just a few weeks of data collection at LAPP's own test center, cable failure was predicted within a few hours to several days notice. If cable failure is predictable, replacement can be planned: the maintenance technician and replacement part can be ready during a period when the machine is not running anyway, for example during machine re-tooling or other maintenance processes.
At Hannover Messe 2019, LAPP presented the new predictive maintenance system in its futureLab. "We are in discussions with a number of interested parties and pilot customers with whom we want to integrate our solution into the specific applications and tailor it to the customer," says Guido Ege. "In the next step we want to develop a suitable business model.”
Guido Ege, Head of Product Development and
Management at LAPP
LAPP owes its success not least to a new innovation process: Innovation for Future. In this way, the company also wants to realize radical and disruptive innovations for which, for example, a classic stage-gate process is unsuitable. Innovation for Future has three prerequisites: There must be a technical solution, you must talk to at least one potential customer, and a business model canvas must be filled in. Guido Ege is optimistic that LAPP will change profoundly with Innovation for Future and that it will continue to evolve from a provider of physical products to a provider of system solutions. "Innovation for Future creates the space for this.”
The team responsible for developing the PMBx won the LAPP internal innovation award, the Eddie Lapp award. “This is really innovation and industry 4.0”, said Ralf Moebus, Head of Product Management Industrial Data Communication about the project.