Electronic Circuit Health Estimation Through Kernel Learning

Abstract

Degradation of electronic components is typically accompanied by a deviation in their electrical parameters from their initial values, which can ultimately lead to parametric faults in electronic circuits. Existing approaches to predict parametric faults emphasize identifying monotonically deviating parameters and modeling their progression over time. However, in practical applications, where the components are integrated into a complex electronic circuit assembly, product, or system, it is generally not feasible to monitor component-level parameters. To address this problem, a circuit health estimation method was developed using a kernel-based machine-learning technique. This method exploits features that are extracted from responses of circuit-comprising components exhibiting parametric faults, instead of the component-level parameters. The method was evaluated using data from simulation experiments on a benchmark Sallen-Key filter circuit and a dc-dc converter system.