Predictive maintenance is part of Industry 4.0 and the factory of tomorrow. For this sector, igus has developed a family of products under the name ‘isense‘, where various sensors and monitoring modules make the plastic components of energy chains, cables and plain bearing products intelligent.
The smart plastics help to avoid unplanned system downtimes and thus expensive production losses. “Thanks to the digital networking of machines and products, it is possible to continuously record the condition of the components and to inform as soon as a repair or replacement is required,” explains Michael Blaß, authorised officer e-chain systems at igus. In energy chain systems this is, for example, the isense EC.M sensor, which is mounted on the moving end of the chain and automatically records acceleration, speed, temperature and the number of cycles completed. The distance travelled and the remaining service life of the system can then be derived from this information. With the optimised isense EC.W sensor, the wear on the wear pads is determined to the nearest percent in sliding e-chain applications on cranes, linear robots or travel axes.
Immediate stop during incidents
The company igus has developed the isense EC.B module for immediate and unpredictable incidents. It determines the breakage of a chain link due to accidents or deliberate destruction. The module consists of a polymer wire in special separators and a sensor unit. The information from the sensor unit can either be read out by the evaluation unit isense EC.B, which may be part of the IoT network, or the sensor unit is connected directly to the customer’s system. Among other things, this system is used by an Austrian automotive supplier in their indoor linear robot for the automated handling of engine blocks. Here, in the past, there were no complaints to be detected in the visual monitoring of the energy chain systems, but the chain failed two weeks later. This can then be prevented by the break monitoring. Since 2010, the so-called PPDS (Push/Pull Force Detection System) has been used worldwide on hundreds of crane systems to detect unusual operating conditions. A force sensor attached to the moving end of the e-chain continuously measures the resulting tensile and shear forces during operation and balances them with the saved set points. In the event of a fault, an immediate shutdown of the system takes place, depending on the settings made by the customer, before further major damage to the e- chain system occurs.
The proven PPDS concept has now been integrated into the isense concept under the name isense EC.P. In addition, the modules can be integrated wirelessly via WLAN or wired (CAN bus) and thus also help to improve the preventive maintenance recommendations. For energy chains and in particular in guide troughs on long travels, isense EC.RC (e-chain run control) provides reliable protection against rising in the event of a blockage. Sensors measure and check the position of the energy chain. In this way, the machine is prevented from continuing to operate or the chain from rising when mechanical faults occur; meaning that total loss of the chain or an electrical shutdown (for example, due to cable damage) are a thing of the past.
With the isense CF.Q top-hat rail module even chainflex cables, which were developed by igus specifically for the moving application in energy chains, become “intelligent”. Based on the experience of thousands of tests in the world’s largest laboratory for dynamic cables spread over a floor area of 2,750 m2, they advise the customer before the changes in the electrical properties lead to a plant shutdown. Due to continuous measurement of the electrical properties, ambient temperature and the number of cycles, a possible failure of the cable is predicted in good time. Continuous testing in the test laboratory and in customer applications help to make analysis of the measured values increasingly more precise.
Intelligent plain bearing technology
There are members of the isense family also in the plain bearing technology division, the second largest business area of igus. With the isense PRT.W module and the isense DL.W module, the abrasion and thus the remaining service life can be measured on iglidur PRT polymer slewing ring bearings or drylin linear guides. The status data are passed on via wireless to the user, who is able to intervene at the right time from anywhere. As lightweight and robust plastic elements, these modules are also suitable for easy retrofitting and a variety of applications. For example, particularly in packaging technology, but also in assembly automation, automobile factories or in the manufacture of electronic articles.
Collecting and evaluating machine data
In Industry 4.0 networks, isense products can send all collected data anonymously to a central igus data store via an icom data concentrator. The advanced icom module now communicates extensively without wiring. It is even easier to integrate into existing production, one of the reasons being that only a single icom module is needed for several systems. In addition, customers can optionally connect status-monitoring, data-generating units of other manufacturers to the icom module. From these data, the igus Machine Learning models calculate a wear prediction that takes into account the actual usage patterns. The precision of the maintenance recommendations presented via a web interface is thus continuously improved. The plant operator can then make use of planned production stoppages to implement precise preventive maintenance recommendations and thereby optimise their business processes.
In this way, the maintenance and service costs can be further reduced and the quality of the production enhanced. The collection and analysis of machine data is in the process an important cornerstone for a higher planning reliability and efficient maintenance in the factory of the future. Comparable to a global test laboratory, the analysis and evaluation of concrete data from a wide range of applications allows the identification of numerous statistical values which, on the one hand, make more precise predictions possible and on the other, could also be integrated into the research and development of new products.