Fault Diagnosis of DC/DC Buck Converter for Embedded Applications Based on BO-ELM

With the increasing demand for ocean missions, the development of autonomous marine vehicles (AMVs) has flourished. AMVs rely on electricity to power various systems, so the high reliability and stability of dc-dc converters are critical to the normal operation of AMVs. In reality, fault diagnosis and optimization of converters under the low sampling frequency and computational capability conditions remain practical challenges. This article focuses on the buck converter circuit and utilizes undersampling techniques to obtain output voltage signals sampled at a low sampling frequency. Support vector machine recursive feature elimination is employed for feature selection to reduce computation. Bayesian optimization-based extreme learning machine is used for fault diagnosis, which is suitable for actual deployment and shown to outperform three classical machine learning models. The diagnosis results are used to propose a reliability optimization strategy involving switching frequency adjustment. Physical experiments based on a digital signal processor prove the feasibility of this method.