Intelligent Detection for Key Performance Indicators in Industrial-Based Cyber-Physical Systems
Intelligent Detection for Key Performance Indicators in Industrial-Based Cyber-Physical Systems
复制标题
工业信息物理系统关键性能指标的智能检测
DOI：
10.1109/tii.2020.3036168
复制
发表时间：
2021-08-01
影响因子：
12.3
通讯作者：
Xiong, Neal N.
中科院分区：
文献类型：
--
作者：
He, Shiming;Li, Zhuozhou;Xiong, Neal N.
关键性能指标（KPI）的智能异常检测对于保持基于工业的网络物理系统（CPS）中的服务可靠性非常重要。然而，在实践中通常使用各种KPI采样策略。我们通过实验验证了异常检测对不规则采样的高度敏感性，并相应地继续研究大规模不规则KPI的低成本异常检测。不规则KPI可以分为四种类型：等间隔不等数量（EIUQ）KPI、不等间隔（UI）KPI、不等间隔等持续时间（UIED）KPI和分段不规则KPI。在这篇文章中，我们提出了一个基于这些不规则类型的异常检测框架。此外，为了处理EIUQ KPI之间的各种长度和相移，我们提出了一个归一化的不等互相关版本，它可以滑动KPI以找到最相似的位置。为了避免高的计算成本，我们分析的低秩特性的KPI数据，并提出了一种基于矩阵分解的对齐算法的UIED的KPI，该算法将UIED的KPI作为一个不完整的矩阵，并恢复的KPI进行对齐之前执行异常检测。使用三个公共数据集和两个真实世界数据集的大量模拟表明，我们的算法可以实现比Minkowski距离更大的F1分数，比动态时间弯曲距离更少的时间。
Intelligent anomaly detection for key performance indicators (KPIs) is important for keeping services reliable in industrial-based cyber-physical systems (CPS). However, it is common in practice for various KPI sampling strategies to be utilized. We experimentally verify that anomaly detection is highly sensitive to irregular sampling, and accordingly go on to investigate low-cost anomaly detection for large-scale irregular KPIs. Irregular KPIs can be classified into four types: equal interval and unequal quantity (EIUQ) KPIs, unequal interval (UI) KPIs, unequal interval with equal duration (UIED) KPIs, and segmented irregular KPIs. In this article, we propose an anomaly detection framework based on these irregular types. Moreover, to handle the various lengths and phase shifts among EIUQ KPIs, we propose a normalized version of unequal cross-correlation, which slides the KPIs to enable finding the most similar position. To avoid high computational costs, we analyze the low-rank feature of KPIs data and propose a matrix factorization-based alignment algorithm for UIED KPIs; this algorithm treats UIED KPIs as an incomplete matrix and recovers the KPIs to align them before performing anomaly detection. Extensive simulations using three public datasets and two real-world datasets demonstrate that our algorithm can achieve a larger F1-score than Minkowski distance and less time than dynamic time warping distance.