关键词: 建筑节能, 冷水机组, 故障诊断, 参数残差, 贝叶斯网络

Abstract: Chillers, as a major consumer, account for about 40% of the total building energy consumption. However, the failure of chillers will lead to additional energy waste accounting for 15%~30% of building energy consumption. Therefore, applying fault diagnosis technology to chiller plays an important role in reducing energy consumption and improving operation efficiency. On the other hand, considering that the residuals of parameters involve more information reflecting faults, a fault diagnosis method based on parameter residual-driven Bayesian Network (BN) is proposed by combining parameter residuals with BN for chiller, in order to furtherly improve the fault diagnosis performance. Being different from most of the conventional methods directly using the parameter measurement values to train the models, the proposed method uses the residuals, calculated through the actual values and reference values of parameters to train the BN model, thus to make full use of the fault information contained in parameter residuals. To evaluate comprehensively the effectiveness of parameter residuals referring to enhance the diagnostic performance, three models used to determine the reference values are developed and compared. Two are linear analysis methods, i.e., multivariate linear regression (MLR) and partial least squares regression (PLSR), and the other is a nonlinear analysis method, i.e., back-propagation neural network (BPNN). Finally, the proposed method based on parameter residual-driven BN is applied to a real experimental chiller, and the experimental data are used to verify its effectiveness. The results show that: (1) Compared with the diagnosis model driven by parameter measurement values directly, the proposed method has higher diagnosis accuracies for the considered seven common faults of chiller, and the diagnosis accuracy is increased by 22.51 percentage point at most; (2) Compared with the reference model based on MLR and PLSR, the diagnosis performance is better when the BPNN model is used to determine the reference value, and the diagnosis accuracy is increased by 12.35 and 12.05 percentage point at most, respectively; (3) The proposed method can effectively improve the diagnostic performance, especially for these faults at slight severity level.

Key words: building energy conservation, chiller, fault diagnosis, parameter residuals, Bayesian network