The overall detection rate using conventional neural networks to detect and diagnose the chillers’ fault is low

even this method can’t detect the fault completely. In order to improve the fault detection and diagnostic accuracy of chiller

an improved neural network fault detection strategy based on Bayesian regularization is proposed. Due to the defects of poor generalization ability of BP neural network

the neural network based on Bayesian regularization can improve the detection efficiency of the model. Bayesian algorithm by limiting the weights of the neural network makes the network more smooth

which make the model more precise. Validation of FDD (fault detection and diagnosis) strategy through using ASHRAE Project data shows that the detection rate is improved obviously.