Fault detection (FD) for building energy systems in the presence of missing data is discussed in this paper. The purpose is to propose an enhanced FD method with higher accuracies under both missing univariate data and multivariate data. The solution is to develop an effective missing data imputation model with low complexity and high computational efficiency to impute the missing values. An FD method based on expectation–maximization (EM) algorithm and Bayesian network (BN), which is called EM-BN method, is presented. The EM algorithm is utilized to impute the missing data, thus to keep the information hidden by the missing data. The imputed complete data sets are addressed with maximum likelihood estimation to obtain the parameters of BN. The presented method is evaluated using the experimental data. Test results show that (i) compared with the method discarding the missing data, the proposed EM-BN method significantly improves the FD accuracies from 55.9% to 96.3% at most (for refrigerant overcharge at severity level 3); (ii) compared with the method using back-propagation neural network (BPNN) to impute the missing data, the proposed EM-BN method significantly reduces the model complexity and improves computational efficiency, particularly under the missing multivariate data.

本文讨论了在缺少数据的情况下建筑能源系统的故障检测（FD）。目的是提出一种在缺失单变量数据和多变量数据的情况下具有较高准确性的增强型FD方法。解决方案是开发一种有效的缺失数据插补模型，该模型具有较低的复杂度和较高的计算效率来估算缺失值。提出了一种基于期望最大化（EM）算法和贝叶斯网络（BN）的FD方法，称为EM-BN方法。EM算法用于估算丢失的数据，从而使信息被丢失的数据隐藏。用最大似然估计来处理估算的完整数据集，以获得BN的参数。利用实验数据对提出的方法进行了评估。测试结果表明：（i）与丢弃丢失数据的方法相比，拟议的EM-BN方法将FD的精确度从55.9％最多提高到96.3％（对于严重性级别3的制冷剂过充）；（ii）与使用反向传播神经网络（BPNN）插补缺失数据的方法相比，所提出的EM-BN方法显着降低了模型的复杂性并提高了计算效率，尤其是在缺失多元数据的情况下。