A fault in a multivariate system is usually attributed to abnormal changes of only a small part of variables. For such a fault, the fault detection index that is defined using all variables may not have a good detection performance, due to the amplification and masking effects caused by fault-free variables. To overcome this problem, this paper proposes a multigroup fault detection and diagnosis (FDD) scheme for multivariate systems. This scheme consists of two main parts: A method for the grouping of variables, and a method to use variable groups for online FDD. In the variable grouping method, the closely correlated variables are grouped together, because the close correlations among variables are proved to be advantageous to FDD. In the online FDD method, a key group to FDD is adaptively selected for every new sample, and then FDD is performed in the key group using two types of fault detection indices that take into account the intragroup and intergroup variable correlations, respectively. Because online FDD is carried out only in one variable group, the multigroup FDD scheme has two advantages. First, the fault detection capability is improved by reducing the amplification and masking effects caused by variables in other groups. Second, fault diagnosis becomes easier because the search scope of faulty variables is narrowed down to members of the key group. These two advantages are illustrated with two case studies.

中文翻译：

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多元系统的多组故障检测与诊断方案

多元系统中的故障通常归因于仅一小部分变量的异常变化。对于此类故障，由于无故障变量引起的放大和掩盖效果，使用所有变量定义的故障检测指标可能无法具有良好的检测性能。为了克服这个问题，本文提出了一种用于多变量系统的多组故障检测和诊断（FDD）方案。该方案包括两个主要部分：一种用于变量分组的方法，以及一种用于在线FDD使用变量组的方法。在变量分组方法中，将紧密相关的变量分组在一起，因为事实证明变量之间的紧密相关对FDD有利。在在线FDD方法中，为每个新样本自适应选择FDD的关键组，然后使用两种类型的故障检测指标在关键组中执行FDD，这两种故障检测指标分别考虑了组内变量和组间变量的相关性。由于在线FDD仅在一个变量组中执行，因此多组FDD方案具有两个优点。首先，通过减少由其他组中的变量引起的放大和掩蔽效果来提高故障检测能力。其次，由于故障变量的搜索范围缩小到关键组的成员，故障诊断变得更加容易。这两个优点通过两个案例研究得以说明。多组FDD方案有两个优点。首先，通过减少由其他组中的变量引起的放大和掩蔽效果来提高故障检测能力。其次，由于故障变量的搜索范围缩小到关键组的成员，故障诊断变得更加容易。这两个优点通过两个案例研究得以说明。多组FDD方案有两个优点。首先，通过减少由其他组中的变量引起的放大和掩蔽效果来提高故障检测能力。其次，由于故障变量的搜索范围缩小到关键组的成员，故障诊断变得更加容易。这两个优点通过两个案例研究得以说明。