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BRICK-Automated Virtual Temperature Sensors for Sensor Fault Detection, Isolation, and Discrimination in

Khaled Chahine1, Hassan N Noura2,3

  • 1College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary

This study introduces a novel framework for detecting subtle sensor bias faults in HVAC systems. The method uses virtual sensors and machine learning to achieve highly accurate fault detection and diagnosis, outperforming existing techniques.

Keywords:
BRICK schemaHVACLightGBMbuilding metadatafault discriminationfault isolationinter-sensor consistencysensor fault detectionsmart buildingsvirtual sensor

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Area of Science:

  • Building Science
  • Data Science
  • Control Systems Engineering

Background:

  • Sensor bias faults in closed-loop HVAC systems are challenging to detect due to control loop compensation, leading to hidden anomalies.
  • These anomalies are redistributed across correlated sensors, compromising system consistency and requiring advanced detection methods.

Purpose of the Study:

  • To propose a framework for automated virtual sensor model derivation using BRICK schema metadata for HVAC sensor fault detection.
  • To develop a robust method for detecting, isolating, and discriminating sensor bias faults in HVAC systems.
  • To evaluate the proposed method's performance and scalability across different HVAC system types.

Main Methods:

  • Automated derivation of virtual temperature sensor models from BRICK schema metadata.
  • Training LightGBM regressors on fault-free inter-sensor relationships to generate anomaly detection signals.
  • Fault isolation via ranking sensors by median daily anomaly scores and fault-type discrimination using actuator command-position discrepancies.

Main Results:

  • Achieved an AUC of 0.9992 for mildest sensor bias (+2 °C) and 1.0 for other scenarios on the LBNL FDD benchmark.
  • Demonstrated superior performance compared to Principal Component Analysis (PCA) on Fan Coil Unit (FCU) systems (AUC = 1.0 vs. 0.63-0.90).
  • Confirmed robustness with AUC remaining above 0.997 across 10 random seeds and scalability across different HVAC system types (SD-AHU and FCU).

Conclusions:

  • The proposed BRICK-automated virtual sensor construction is a viable and scalable approach for deployment-ready sensor validation in smart building HVAC systems.
  • The method effectively detects, isolates, and discriminates sensor bias faults, offering significant advantages over traditional methods like PCA.
  • The framework's ability to handle diverse HVAC systems without modification highlights its cross-system scalability and practical applicability.