Culemborg – The Netherlands March 16th 2018. A great new wealth of opportunities is opening up for machine builders and asset owners. UVS Industry Solutions is launching sensor technology specifically designed for predictive maintenance. Making it easy to implement and retrieve measured high-resolution data for a wide variety of parameters like vibration, temperature and current without wiring or reprogramming your machine.

Traditional automation technology strongly focuses on real-time operation and safeguarding of systems however from a maintenance perspective the need to know the (electro) mechanical state or component degradation in time are relevant.

The ERL and EDVL series SMART Sensor technology provide a means to sense a wide variety of parameters, storing this data locally (inside the sensor) and communicate state and degradation changes only when relevant or requested via LoRa, GSM or WiFi. The battery charged sensors combines 3-axis vibration, gyroscopic, humidity, and temperature sensing from one housing at 16bit 8kHz resolution, which makes it possible to draw a detailed picture of asset behavior in it’s context of operation. Additionally current, axis rotation and 4-20mA signals can be connected to the sensor giving it’s primary measurements even more context.

UVS Industry Solutions specializes in predictive maintenance and offers a complete vertical solution from sensor up to boardroom statistical data boards.

The new sensor range makes it possible to permanently or temporarily measure parameters that reveal degradation or system decline well in advance. In combination with our predictive maintenance platform, machine builders and asset owners are now able to engage into new business models like servitization and adaptive maintenance where system availability is key.

At Hannover MESSE, UVS Industry Solutions as part of the Holland IT & Smart Industry pavillion Hal 8 D38 is showing this new and innovative way of measuring and disclosing machine information that really helps to make the NEXT step into predictive maintenance.

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