Together with the thermo-physical relationships among the flow rates and temperatures of water in a piping system, the Bayesian method was employed to develop a model for detection and evaluation of biases of water flow and temperature sensors in a central chiller plant. The model can handle biases of multiple sensors occurring simultaneously and can remain functional when the coverage of the available measurements is incomplete. A series of case studies was done to verify the performance of the model and for comparison with the conventional method that is based solely on the thermo-physical relationships. The cases studied involved the use of synthetic plant operating data and actual operating records of an existing chiller plant. In this paper, the theoretical basis of the model is outlined, and explanations are given for the superior performance of the Bayesian method in handling cases with data that cannot fully cover the required range of operating chiller patterns. Results of the cases unveiled the effects of the prior belief with or without being updated during the estimation process, and of biases occurring in steps at the same time and at different times, as well as those that would increase with time. Furthermore, the case studies showed that the Bayesian method was able to detect sensor biases of a magnitude of ± 0.5 °C or lower.

结合管道系统中水的流量和温度之间的热物理关系，采用贝叶斯方法开发了一个模型，用于检测和评估中央冷水机组中水流和温度传感器的偏差。该模型可以处理同时发生的多个传感器的偏差，并且在可用测量范围不完整时可以保持功能正常。进行了一系列案例研究，以验证模型的性能，并与仅基于热物理关系的传统方法进行比较。研究的案例涉及使用合成工厂的运行数据和现有冷水机组的实际运行记录。本文概述了该模型的理论基础，并给出了贝叶斯方法在处理数据不能完全覆盖所需的运行冷却器模式范围的情况下的优越性能的解释。案例的结果揭示了先验信念的影响，在估计过程中是否进行了更新，以及在同一时间和不同时间分步出现的偏差，以及随时间增加的偏差。此外，案例研究表明，贝叶斯方法能够检测幅度为±0.5°C或更低的传感器偏置。以及在同一时间和不同时间分步出现的偏差，以及随着时间而增加的偏差。此外，案例研究表明，贝叶斯方法能够检测幅度为±0.5°C或更低的传感器偏置。以及在同一时间和不同时间分步出现的偏差，以及随着时间而增加的偏差。此外，案例研究表明，贝叶斯方法能够检测幅度为±0.5°C或更低的传感器偏置。