ECGAN: An Efficient Diagnostic Strategy for Hidden Deterioration in DC-DC Converters

Abstract

This article highlights the critical role of reliable dc-dc converter operation in ensuring the stability of power conversion systems, especially in extreme environments like underwater observation networks. Addressing the common challenge in dc-dc converter fault diagnosis—overlooking the periodic characteristics of electrical signals during feature extraction from large datasets—we propose an innovative diagnostic method that combines adaptive wavelet transform (AWT) and an enhanced classifier generative adversarial network (ECGAN). First, AWT dynamically selects and optimizes WBFs to accurately capture degradation features in the output voltage signals. Then, a compound rainbow convolution block (CRConvBlock) is used to enhance the time-frequency representation of these signals, integrating temporal and frequency information for improved feature extraction. Furthermore, the proposed ECGAN model is guided by an adaptive loss optimization framework (ALOF) that dynamically adjusts training weights to balance sample quality and classification accuracy. Experimental validation in four different circuits demonstrates the high accuracy and robustness of the method, highlighting its potential for practical engineering applications.