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<p>The virtual in-situ calibration method has been effective in calibrating multiple sensors in HVAC systems when the fault types are known. However, due to the high cost of physical calibration and the strict data accuracy requirements of data-driven methods, obtaining benchmarks for sensors during actual operation is challenging, making it difficult to diagnose the specific fault types of the sensors. To address this issue, an enhanced multi-sensor calibration (EMC) method has been developed to operate without prior knowledge of fault types. The primary soft faults encountered—bias and drift deviations—differ in whether they vary over time. The proposed method employs an interval sliding approach to identify and calibrate these faults within each interval effectively. Furthermore, the influence of interval size on calibration accuracy has been systematically analyzed to optimize performance. The proposed method has been validated on a chiller plant in a large public building in Hong Kong. The experimental results indicate that, under conditions involving eight sensor faults, including even three drift deviations, the EMC method achieved average calibration accurate rates of 100% for bias faults and 95% for drift faults. Notably, in calibrating drift faults, the enhanced method outperformed the high-dimensional sensor calibration method and the Improved simulated annealing method by 87% and 34%, respectively.</p>

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An enhanced multi-sensor calibration method for heating, ventilation, and air conditioning systems without prior knowledge of fault types

  • 期刊论文

  • 发表日期：

    2025年6月5日

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  • Kai Hu,
  • Chengchu Yan,
  • Junjian Fang,
  • Yizhe Xu,
  • Rui Zhang,
  • Chaoqun Zhuang

摘要

The virtual in-situ calibration method has been effective in calibrating multiple sensors in HVAC systems when the fault types are known. However, due to the high cost of physical calibration and the strict data accuracy requirements of data-driven methods, obtaining benchmarks for sensors during actual operation is challenging, making it difficult to diagnose the specific fault types of the sensors. To address this issue, an enhanced multi-sensor calibration (EMC) method has been developed to operate without prior knowledge of fault types. The primary soft faults encountered—bias and drift deviations—differ in whether they vary over time. The proposed method employs an interval sliding approach to identify and calibrate these faults within each interval effectively. Furthermore, the influence of interval size on calibration accuracy has been systematically analyzed to optimize performance. The proposed method has been validated on a chiller plant in a large public building in Hong Kong. The experimental results indicate that, under conditions involving eight sensor faults, including even three drift deviations, the EMC method achieved average calibration accurate rates of 100% for bias faults and 95% for drift faults. Notably, in calibrating drift faults, the enhanced method outperformed the high-dimensional sensor calibration method and the Improved simulated annealing method by 87% and 34%, respectively.