Abstract Traditional anomaly detection procedures assume that normal observations are obtained from a single distribution. However, due to the complexities of modern industrial processes, the observations may belong to multiple operating modes with different distributions. In such cases, traditional anomaly detection procedures may trigger false alarms while the process is indeed in another normally operating mode. We propose a generalized support vector-based anomaly detection procedure called generalized support vector data description which can be used to determine the anomalies in multimodal processes. The proposed procedure constructs hyperspheres for each class in order to include as many observations as possible and keep other class observations as far apart as possible. In addition, we introduce a generalized Bayesian framework which does not only consider the prior information from each mode, but also highlights the relationships among the modes. The effectiveness of the proposed procedure is demonstrated through various simulation studies and real-life applications.

中文翻译：

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异常检测的通用支持向量数据描述

摘要 传统的异常检测程序假设正态观测值是从单一分布中获得的。然而，由于现代工业过程的复杂性，观测值可能属于具有不同分布的多种操作模式。在这种情况下，传统的异常检测程序可能会触发误报，而过程确实处于另一种正常运行模式。我们提出了一种基于广义支持向量的异常检测程序，称为广义支持向量数据描述，可用于确定多模态过程中的异常。所提出的程序为每个类构建超球面，以便包含尽可能多的观察结果并使其他类观察结果尽可能远离。此外，我们引入了一个广义贝叶斯框架，它不仅考虑了每个模式的先验信息，而且还突出了模式之间的关系。通过各种模拟研究和实际应用证明了所提出程序的有效性。