Condition monitoring and predictive maintenance

Predictive Maintenance techniques detect anomalies in equipment before those turn into system-critical failures, allowing maintenance to be scheduled before the equipment actually breaks down. This increases equipment uptime reduces overall maintenance costs and allows optimization of spare part inventory by enabling preventive maintenance based on the equipment’s actual needs. 

After having proven its benefits in the context of Industry 4.0., this data-driven maintenance strategy is now being adopted in the Smart Buildings of tomorrow. Especially for infrastructure critical assets such as HVAC, Predictive Maintenance offers many benefits.

Condition monitoring facilitates insights into the actual state of devices in buildings by making use of sensors. They collect data on an equipment’s most crucial parameters by enabling vibration analysis, sound anomaly detection, current sensing, and the monitoring of other parameters to gain meaningful insights into equipment’s health.

Infineon’s broad range of XENSIV™ sensors enables accurate and reliable data collection for condition-based monitoring. This way, crucial parameters in HVAC equipment such as current, vibration, or airflow in motors, compressors, and fans can be monitored. Along with FreeRTOS certified XMC™ microcontrollers and OPTIGA™ security solutions collected data can be processed and analyzed to enable advanced IoT features such as Predictive Maintenance. Thanks to our Semper™ NOR Flash Memory Family all collected data can be reliably and safely stored.

HVAC units are part of a building’s critical assets. An HVAC unit’s fans, motors, and compressors are most susceptible to fail. Information about airflow, current consumption, sound, and vibration can help to understand whether the unit runs without error or whether the failure is likely to occur soon. Together with Infineon’s product, those critical parameters can be monitored:

• 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-A2B6 3D magnetic sensor
• Sound anomaly detection in the unit with the XENSIV™ IM69D130 MEMS microphone
• Linear vibration measurement with XENSIV™ TLE4997E Linear Hall sensor
• Opened and closed lid detection with XENSIV™ TLE4964-3M Hall sensor
• Speed and direction measurement with XENSIV™ TLI4966G Double Hall sensor
• Data processing with XMC4700
• Secured connection and authentication with OPTIGA™ Trust M