Not every position sensor can do this. Magnetic and optical sensors read position; an inductive sensor responds to the target's full motion trajectory — and that richer signal carries information not just about position, but about how the mechanics behind it are changing: growing backlash, abnormal friction, early signs of wear, which show up as misalignments.
The EMC Gems iEncoder turns this into condition monitoring and predictive maintenance, adding diagnostics without adding a single component to the machine.
Condition monitoring without added hardware is a prerogative of inductive sensing, and our patent-pending technology is what makes it usable in a real product. The approach pairs the iEncoder with physics-based digital twins of the healthy machine, so deviations from normal behavior are detected as they emerge.
Conventional condition monitoring means installing extra sensors — accelerometers, eddy current linear proximity sensors — with their own wiring, cost and points of failure. Our approach needs none of that: the diagnostic signal comes from the position sensor that is already there to do its primary job.
No additional hardware, no additional weight, no extra installation. For systems where space, weight and reliability are tightly constrained, monitoring that comes for free with the sensor is a fundamentally different proposition.
A motor's own encoder sits upstream of the gearbox and reports where the motor thinks it is — not where the load actually is. Because the iEncoder measures displacement directly, on the slow side of the mechanics, it captures backlash, elastic deflection and wear that are invisible to motor-side feedback.
Our digital twins are not generic data models. They are grounded in the same validated electromagnetic and mechanical physics as our sensors and simulation platforms — so the fault signatures the system learns to recognize can be generated by simulation, without collecting data from broken machines. That physical grounding is what lets it tell a real fault from noise, and explain what it sees rather than just flag an anomaly.
By comparing real motion against the model of the healthy machine, emerging faults are detected before they cause failures — turning maintenance from a fixed schedule or an emergency into a planned, data-driven decision.
For systems that sit idle for long periods and must work on the first command, built-in diagnostics can verify mechanical health without a dedicated test bench — a capability with particular value in defense and other high-reliability applications.
Base maintenance and operational decisions on current, ready-to-use information from the machine itself, rather than on fixed intervals or manual inspection — reducing unplanned downtime and improving productivity and product quality.
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Cap. Soc. € 10.000,00 i.v.