Degradation Detection of a DC/DC Buck Converter Based on Feature Extraction

DC/DC converters play a crucial role in electronic systems by providing the necessary power support and ensuring the proper operation of devices. During operation, the converter is exposed to environmental factors such as temperature and humidity, leading to component degradation and subsequently posing a threat to system reliability and functionality. This paper presents an effective degradation detection method for DC/DC converters using feature extraction techniques. The output voltage ripple is used as the degradation indicator, and its values under healthy and degradation conditions are collected. Based on the time domain, frequency domain, and time-frequency domain feature information of the output voltage ripple, a valid feature selection method is designed to select key features, and the Bayesian optimization-based support vector machine is established to detect degradation using the selected key features. Since no extra hardware circuits or elaborate mathematical models are required, the proposed method has low cost and complexity. Simulation and experimental results show the validity and practicability of the proposed method.