Predictive maintenance test and measurement kit for sensor-based condition monitoring introduced by Infineon

March 16, 2021
Kit has sensor, microcontroller, and security for heating, ventilation, air conditioning, motors, fans, drives, and compressors in smart buildings.

MUNICH – Infineon Technologies AG in Munich is introducing the XENSIV predictive maintenance evaluation kit for quick and easy test and measurement of sensor-based condition monitoring and predictive maintenance.

The XENSIV kit was co-developed with the Internet of things (IoT) service provider Klika Tech and is powered by the cloud service provider AWS to offer an end-to-end solution.

The kit includes sensor, microcontroller, and embedded security hardware; software; and CloudFormation templates. Target applications involve heating, ventilation, and air conditioning equipment as well as motors, fans, drives, compressors, refrigeration, and other components of smart buildings.

The XENSIV kit is an extension for the XMC4700 XMC relax kit. It can use XENSIV sensor satellite boards with sensors for data collection and condition monitoring.

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Other applications include airflow measurement at the compressor based on the XENSIV DPS368 barometric pressure sensor; current measurement at the fan and compressor based on the XENSIV TLI4971 current sensor; position sensing of the motor with XENSIV TLI493D-W2BW 3D magnetic sensor; sound anomaly detection in the unit with the XENSIV IM69D130 MEMS microphone; and linear movement vibration measurement with XENSIV TLE4997E Linear Hall sensor.

The software provided supports the FreeRTOS kernel. AWS Cloud integration is completed by full AWS CloudFormation templates and a software application stack.

A graphic user interface and basic anomaly detection for test and measurement also are included. For connectivity Wi-Fi and Ethernet are integrated on-board, as well as a mikroBUS ClickBoard interface for extended connectivity. Multi-account registration is supported with OPTIGA Trust M embedded security solution.

For more information contact Infineon online at www.infineon.com/predictivemaintenance.

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