论文标题
使用自动编码器的CPP数据降低和异常检测
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder
论文作者
论文摘要
无监督的异常检测（AD）是网络物理生产系统（CPPSS）领域的主要主题。密切相关的关注点是降低维度（DR），即：1）通常用作AD解决方案中的预处理步骤，2）一种AD，如果提供了与学习数据歧管的观察量的量度。 我们认为，这两个方面可以在CPPS异常检测解决方案中互补。在这项工作中，我们专注于非线性自动编码器（AE）作为DR/AD方法。这项工作的贡献是：1）我们研究了AE重建误差作为CPPS数据中的AD决策标准的适用性。 2）我们分析了AE潜在空间中潜在的第二阶段AD方法的关系3）我们评估了三个现实世界数据集上该方法的性能。此外，该方法的表现优于最先进的技术，以及相对简单明了的应用程序。
Unsupervised anomaly detection (AD) is a major topic in the field of Cyber-Physical Production Systems (CPPSs). A closely related concern is dimensionality reduction (DR) which is: 1) often used as a preprocessing step in an AD solution, 2) a sort of AD, if a measure of observation conformity to the learned data manifold is provided. We argue that the two aspects can be complementary in a CPPS anomaly detection solution. In this work, we focus on the nonlinear autoencoder (AE) as a DR/AD approach. The contribution of this work is: 1) we examine the suitability of AE reconstruction error as an AD decision criterion in CPPS data. 2) we analyze its relation to a potential second-phase AD approach in the AE latent space 3) we evaluate the performance of the approach on three real-world datasets. Moreover, the approach outperforms state-of-the-art techniques, alongside a relatively simple and straightforward application.
