Identifying outlier behavior among sensors and subsystems is essential for discovering faults and facilitating diagnostics in large systems. At the same time, exploring large systems with numerous multivariate data sets is challenging. This study presents a lightweight interconnection and divergence discovery mechanism (LIDD) to identify abnormal behavior in multi-system environments. The approach employs a multivariate analysis technique that first estimates the similarity heatmaps among the sensors for each system and then applies information retrieval algorithms to provide relevant multi-level interconnection and discrepancy details. Our experiment on the readout systems of the Hadron Calorimeter of the Compact Muon Solenoid (CMS) experiment at CERN demonstrates the effectiveness of the proposed method. Our approach clusters readout systems and their sensors consistent with the expected calorimeter interconnection configurations, while capturing unusual behavior in divergent clusters and estimating their root causes.

该研究提出了一种轻量级的互连和差异发现机制（LIDD），用于识别多系统环境中的异常行为，通过多变量分析技术估计传感器之间的相似热图，并应用信息检索算法提供相关的多级互连和差异细节。在CERN的紧凑双曲线磁铁（CMS）实验的Hadron Calorimeter的读出系统上的实验验证了所提出方法的有效性，以符合预期的能量量能器互连配置聚类读出系统和传感器，同时捕获发散聚类中的异常行为并估计其根本原因。

轻量级多系统多元相互连接与差异发现