Bias fault detection of temperature sensor in cold system based on BOA-SVM
ZHOU Xuan
YAN Xue-cheng
YAN Jun-wei
LIANG Lie-quan
Abstract：Aiming at the problem of low fault recognition rate of temperature sensor bias,which seriously affects the energy saving and reliable operation of cold system,a bias fault detection method based on Bayesian optimization algorithm-support vector machine(BOA-SVM)combined optimization algorithm is proposed.This method combines the BOA and the SVM technology,which is suitable for small and nonlinear fault data.At the same time,it overcomes the problem that SVM algorithm is sensitive to kernel parameters and penalty factors.In this paper,a Tmsys simulation model of cold system of an office building in Guangzhou is established to simulate the bias faults of outdoor dry bulb,chilled water supply and cooling water intake of three temperature sensors.Compared with other methods proposed in this paper,the simulation results show that the proposed method has high accuracy,strong generalization ability and robust performance.It can meet the detection requirements of temperature sensor bias fault in cold system and has great significance for ensuring the safe and efficient operation of air conditioning system.
Keywords：cold systemtemperature sensorBayesian optimization algorithm(BOA)support vector machine(SVM)fault detectionTrnsys
Publication Date：2025-05-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 921-930 )