Unsupervised Root-Cause Analysis for Integrated Systems
Unsupervised Root-Cause Analysis for Integrated Systems
复制标题
集成系统的无监督根本原因分析
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Xinli Gu
中科院分区：
文献类型：
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作者：
Renjian Pan;Zhaobo Zhang;Xin Li;K. Chakrabarty;Xinli Gu
集成系统日益增加的复杂性和高成本给根本原因分析和诊断带来了巨大的压力。在人工智能和机器学习的基础上，提出了大量的智能根源分析方法。然而，他们中的大多数需要历史测试数据与根本原因标签从维修历史，这往往是困难和昂贵的获得。在本文中，我们提出了一个两阶段的无监督的根本原因分析方法，其中不需要修复历史。在第一阶段中，使用系统测试信息训练决策树模型以粗略地聚类数据。在第二阶段，频繁模式挖掘应用于提取频繁模式在每个决策树节点，以精确地聚类的数据，使每个集群只代表一个小数目的根本原因。此外，L-方法和交叉验证被用来自动确定我们的算法的超参数。两个行业案例研究与系统测试数据表明，所提出的方法显着优于国家的最先进的无监督的根本原因分析方法。
The increasing complexity and high cost of integrated systems has placed immense pressure on root-cause analysis and diagnosis. In light of artificial intelligent and machine learning, a large amount of intelligent root-cause analysis methods have been proposed. However, most of them need historical test data with root-cause labels from repair history, which are often difficult and expensive to obtain. In this paper, we propose a two-stage unsupervised root-cause analysis method in which no repair history is needed. In the first stage, a decision-tree model is trained with system test information to roughly cluster the data. In the second stage, frequent-pattern mining is applied to extract frequent patterns in each decision-tree node to precisely cluster the data so that each cluster represents only a small number of root causes. In additional, L-method and cross validation are applied to automatically determine the hyper-parameters of our algorithm. Two industry case studies with system test data demonstrate that the proposed approach significantly outperforms the state-of-the-art unsupervised root-cause analysis method.