Intermittent Fault Recognition of Analog Circuits in the Presence of Outliers via Density Peak Clustering With Adaptive Weighted Distance

Intermittent faults (IFs) are the main cause of analog circuit failures, which are short-lived and repeatable. Since IF sample labels are difficult to obtain, unsupervised cluster recognition is an important method to help experts analyze potential classes of IFs. However, outliers generated by noise can affect the recognition of IFs. In this article, an improved density peak clustering (DPC) method is proposed, which enhances the fault recognition performance in terms of both distance and outlier detection. First, an adaptive weighted distance is proposed, which can give different distance weights according to the similarity between two samples. Thus, the outliers have a greater distance from the IF samples and reduce the influence of the outliers on the IF recognition. Second, a new outlier detection strategy is used to improve the DPC algorithm. The new strategy enhances outlier detection and prevents low-density clusters from being misdetected as outliers, thus further reducing the impact of outliers on IF recognition. Finally, the proposed method is applied to two typical analog filter circuits. The results show that this method can effectively recognize IFs in analog circuits.

Automated summary

Intermittent faults (IFs) are short-lived and repeatable failures in analog circuits, which are difficult to label. Unsupervised cluster recognition is an important method to analyze potential classes of IFs. However, outliers from noise can impact the IF recognition. This article proposes an improved density peak clustering (DPC) method that enhances fault recognition by improving distance measure and outlier detection.