本文对Rossi和Braun最初提出的基于统计规则（SRB）的故障检测与诊断（FDD）技术进行了改进。原始方法假设正常和故障操作的协方差矩阵为常数，用于故障检测，而故障诊断为对角协方差矩阵，这导致FDD灵敏度有所损失。作为量化灵敏度损失的第一步，蒙特卡罗模拟（MCS）被用于评估故障概率，使用从屋顶单元上的测量值确定的非对角协方差矩阵。 虽然MSC为离线比较提供了一个很好的基准，但由于计算量大，它不适用于在线应用。为了在不增加计算量的情况下提高SRB方法的灵敏度，开发了两个新的分类器用于FDD方法的检测和诊断步骤。新方法不需要错误操作或概率计算的协方差矩阵，并且实现起来相对简单。此外，还提出了一种更鲁棒的稳态检测器和一种改进的建模方案。 最后，给出了一个案例研究，表明与原始方法相比，整体FDD灵敏度有所提高。本次比较的数据针对的是Breuker和Braun在实验室测试的屋顶空调，该空调具有五种不同的故障类型，在五种故障级别下运行，并在五种不同负载级别下以瞬态模式运行。单位:I-PCITION:研讨会，ASHRAE交易，第109卷，pt。堪萨斯城，2003年

This paper presents improvements to the statistical rule-based (SRB) fault detection and diagnosis (FDD) technique originally presented by Rossi and Braun. The original method assumed a constant covariance matrix for both normal and faulty operations for fault detection and a diagonal covariance matrix for fault diagnosis, which results in some loss of FDD sensitivity. As a first step in quantifying the loss in sensitivity, Monte-Carlo simulation (MCS) was used to evaluate fault probabilities using a nondiagonal covariance matrix determined from measurements on a rooftop unit. Although MSC provides a good benchmark for off-line comparison, it is not practical for on-line application because of large computational demand. In order to improve the sensitivity of the SRB method without increasing the computation, two new classifiers were developed for the detection and diagnosis steps of the FDD method. The new methods do not require the covariance matrix for faulty operation or probability calculations and are relatively straightforward to implement. In addition, a more robust steady-state detector and an improved modeling scheme are proposed. Finally, a case study is presented that demonstrates the improvement in overall FDD sensitivity as compared with the original method. The data for this comparison were for a rooftop air conditioner that was tested by Breuker and Braun in the laboratory with five different fault types, implemented at five fault levels, and operated in a transient mode at five different load levels.Units: I-P