The experts for predictive maintenance
The failure of systems and machines leads to considerable costs every year. The same applies to excessive maintenance with fixed replacement intervals. Components are often replaced that would still have functioned reliably in the next period of use. Predictive maintenance enables needs-based maintenance that allows machine operators to use machines and wear parts in a truly cost- and time-optimized manner. Proactive maintenance based on real process and machine data can prevent expensive breakdowns.
Early detection of overloads or potential failures prevents damage and high costs. However, meaningful and reliable measurement data is essential for these precise predictions. This is precisely what intelligent monitoring systems provide, recording condition data centrally in the machine and system control system and at component level. Predictive maintenance thus becomes a crucial tool for efficient and cost-optimized maintenance.
Predictive maintenance - advantages for your machines and plants
Higher machine availability
Longer use of wear components
Pro-active recommended action to avoid failures
Intervals adapted to requirements
Predictive maintenance based on real load parameters
Real load data directly from the core of an application is invaluable, especially in the context of predictive maintenance. Mechanical loads reveal the basic physical operating principle that can lead to damage. By analyzing this data, it is possible to prevent impending machine failures at an early stage. In contrast, secondary effects such as acceleration and temperature increases are less meaningful, as they react more slowly and are often only recognizable in the event of a fault. They are also more prone to errors and often only function reliably under laboratory conditions.
In complex systems and machines, secondary status variables can change randomly, and a clear correlation with emerging damage cannot always be established under real operating conditions. This can lead to false alarms or, in the worst case, to undetected failures.
In contrast, the actual load data has proven to be a reliable indicator. By taking into account the operationally stable design of components and incorporating extensive empirical values, it is possible to precisely estimate the remaining service life based on the load history. This is crucial for an effective predictive maintenance strategy based on real operating data.
Determine remaining service life directly
Our sensory components can be integrated directly in the load path of a drive train. A sensory drive shaft thus measures, for example, the load variables actually occurring in operation. These real load spectra are then compared with the reference spectra used for design and a damage sum is calculated. If this exceeds a certain threshold value, the component should be replaced. In this way, potential damage is effectively avoided - and this exactly when the replacement of the component was really necessary.
However, a sensor component positioned in the powertrain monitors much more than itself: If the measuring point is cleverly selected, it also detects the loads acting on other components and thus enables their degree of damage to be determined. For example, a sensor in the drive shaft also indirectly monitors components such as gears, clutches or rolling bearings.
So it pays for you to better understand the interactions of your components and processes- and we support you in doing so.
Discover now: Predictive maintenance using the example of monitoring a production plant
Learn more by clicking on the info points
Discover now: Predictive maintenance using the example of monitoring a production plant
Learn more by clicking on the info points
Spindles
Avoid irregular force distribution and the associated overloading of individual spindles with threaded spindles operated in a compound: quite simply by determining the load capacities with the aid of an integrated sensor.
Cardan shafts
Increase the service life of universal joint and cardan shafts: To do this, rely on a sensor integrated in the shaft to record load spectra during operation and, on this basis, make reliable statements about the maximum service life of the components.
Gearbox
Reliably determine the remaining service life of gear units and avoid unnecessary damage in good time: Simply record the load spectra occurring during operation directly in the component using integrated sensors.
Stock
Detect potential damage to rolling bearings long before costly failures occur: To do this, simply determine the axial and radial forces acting via the drive shaft and use this as a basis to determine the actual loads on the bearings.
core sensing - Your partner for your reliable Predicitive Maintenance solution
Better understand the interactions of your components and processes in the future. We support you in this. With a sensor solution developed individually for your needs that enables you to minimize the downtime of your machines and systems and increase their availability. Find out now how you can easily tap into this optimization potential for yourself.
