Unsupervised root-cause analysis with transfer learning for integrated systems
The increasing complexity of integrated systems has exacerbated the problems associated with root-cause analysis. Leveraging advances artificial intelligence, a large amount of intelligent root-cause-analysis methods have been proposed in recent years. However, most of these methods rely on root-cause labels from repair history for defective samples, which are often expensive to obtain. In this paper, we propose an unsupervised root-cause-analysis method that utilizes transfer learning. A two-stage clustering method is first developed by exploiting model selection based on the concept of Silhouette score. Next, a data-selection method based on ensemble learning is proposed to transfer valuable information from a source product to improve the root-cause-analysis accuracy on the target product with insufficient data. Two case studies based on industry designs demonstrate that the proposed approach significantly outperforms other state-of-the-art unsupervised root-cause-analysis methods.
