Abstract：

This paper proposes a multi-sensor calibration method based on a physical correlation to address measurement deviations in central air conditioning water systems caused by sensor aging, environmental corrosion, and other factors. Firstly, the operation principle and monitoring data characteristics of the air conditioning water system are analysed in depth, and the mathematical model of the physical correlations between the sensors is established from the components, equipment and overall system level of the water system. Subsequently, the concept of measurement inconsistency is introduced, and the matching degree of sensor measurements in the physical correlation is evaluated by calculating the inconsistency of each physical correlation and its network, which provides a basis for determining sensor correction functions. Then, the genetic algorithm is used to iteratively find the optimal correction parameters, and the correction value is the benchmark value of the sensor that best conforms to the physical correlation network. Finally, the method is validated with the actual engineering data from a refrigeration machine room in a laboratory in Qingdao. The results show that the proposed method can achieve effective calibration in the face of single sensor faults or complex multi-sensor coupling faults with an average calibration accuracy of over 92%, significantly improve the accuracy of system sensor measurement data, and make the system operation more stable and reliable.

Keywords:central air conditioning water system; online calibration; multi-sensor; physical correlation; genetic algorithm