Highly Reliable Solutions Unleashing The Benefits Of Predictive Maintenance

Avoiding unplanned downtime and operational disruption in building equipment.


Time and again, malfunctions in devices inside a building like HVAC, elevator, refrigeration, and other building systems can lead to severe operational disruption, resulting in increasing maintenance costs and discomfort for its occupants. Predictive maintenance can be an effective way to avoid unplanned downtime. It monitors the condition of equipment in real time and schedules maintenance before failures happen.

Predictive maintenance devices perform the following tasks, from the moment of measurement all the way to data evaluation:

  1. Collecting data using sensors.
  2. When appropriate, aggregation of related sensor data.
  3. Data processing:
    1. Data processing at-the-edge: The data can be processed at-the-edge before being forwarded to a central system such as a cloud platform, or completely autonomous evaluation can take place at-the-edge.
    2. Handover to a central system: Microcontroller technology can hand over the data collected by the sensors to a central building management system or to external cloud services.
  4. Data evaluation: Finally, data is evaluated using the appropriate software tools, including an intelligent software algorithm and predictive maintenance AI. These elements can run independently on the edge.
    1. Software algorithms compare the actual and target status values of the monitored devices, based on sensor data. Any anomalies can be detected, triggering an alarm. Thus the state of the system is recorded and monitored.
    2. A predictive maintenance AI evaluates relevant data using a model based on machine learning and Big Data. Data sources can be manifold: motors, air flow and composition, vibrations, temperatures, etc. are used to detect any correlation between the values. Thanks to artificial intelligence, a prediction can be made as to if and when a failure can be expected. This makes it possible to predict the future state of a system.

The advantages of predictive maintenance in smart buildings

Predictive maintenance in smart buildings offers advantages ranging from the elimination of unnecessary maintenance tasks to tenant comfort and economic efficiency.

  • Cost optimization: Predictive maintenance technology and devices require an initial investment. When used correctly, predictive maintenance offers an attractive ROI by optimizing maintenance costs: Unexpected failures are avoided and operations continue without disruptions. In addition, maintenance measures are conducted at the right time instead of replacing affected parts too early or too often. According to the U.S. Department of Energy, predictive maintenance can reduce maintenance costs by up to one third and cut unplanned downtime by up to three quarters.
  • Individual service ranges: Since smart building data and the condition of a building can be analyzed remotely, all maintenance and service tasks can be outsourced together to external providers as a remote monitoring job. Furthermore, the manufacturers of various smart maintenance devices and systems can offer this as an additional service for their devices. The variety of possibilities lets inhabitants and owners of smart buildings focus fully on their own goals. Meanwhile, service jobs are delegated, planned efficiently and coordinated for the entire building without interrupting building operations.
  • Employee comfort: Another advantage of predictive maintenance is the increased comfort and safety for all occupants. The work environment affects employee satisfaction and ability to concentrate. Regular maintenance of HVAC systems provides consistent, good air quality. In turn, this affects employee health and productivity. Increased safety leads to a decrease in workplace accidents and an increase in employee trust.

Certain prerequisites must be fulfilled within the building or plant to profit from these advantages.

  • Correct installation of the devices: The devices and building infrastructure that will be monitored with predictive maintenance must be correctly installed. Otherwise, the collected data may be incorrect and may lead to incorrect evaluation results, making predictive reliability impossible. The distribution of the devices must also suit the use case: using only a single device to monitor the air quality of each floor offers no true benefit.
  • Evaluating relevant data: Integrating sensors and microcontrollers in new buildings is easy, since an overall concept for the smart building is already possible as early as the building planning phase. On the other hand, in existing buildings, replacing existing technology with predictive maintenance technology requires more effort, since not all devices can be updated at the same time. This makes it necessary to prioritize data relevant to the current building maintenance plan and to understand which data is key to reducing maintenance efforts, in order to update or synchronize the corresponding equipment.
  • Further costs: Although predictive maintenance reduces maintenance and service costs, in addition to the initial investment, predictive maintenance can factor into operational costs, since for example outsourcing data processing may incur new ongoing costs. These may include data storage and processing a central system.

Condition monitoring and predictive maintenance

Typical predictive maintenance examples include HVAC units, which can be mission-critical in healthcare and industrial settings. Experience has shown that HVAC fans, motors, and compressors are the components most susceptible to failure. Information about airflow, current consumption, sound, and vibration can help to understand whether the unit is running smoothly or whether a failure is likely to occur soon. Leveraging Infineon’s products, the following 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 or PSoC 6
  • Secured connection and authentication with OPTIGA Trust M

Get a comprehensive overview of Infineon’s sensor and IoT product portfolio for accurate and reliable data collection and processing for condition monitoring and predictive maintenance at: https://www.infineon.com/predictivemaintenance

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