Unsupervised Two-Stage Root-Cause Analysis for Integrated Systems
Renjian Pan, Zhaobo Zhang, Xin Li, Krishnendu Chakrabarty, Xinli Gu · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2021
The increasing complexity and high cost of integrated systems have placed immense pressure on root-cause analysis and diagnosis. In light of artificial intelligence 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. 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 cluster the data in a coarse-grained manner. 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. The proposed method can accommodate both numerical and categorical test items. A combination of the L-method, cross validation, and Silhouette score enables us to automatically determine all hyperparameters. 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.