A sparse signal decomposition method based on K-SVD dictionary learning is proposed to address the issue of complex vibration signals in shipborne antenna transmission systems. These systems face variable and complex environmental conditions during actual operation, leading to highly nonlinear and non-stationary vibration signals, which increase the difficulty of fault diagnosis and health monitoring. Given the difficulty of traditional parameter dictionaries in matching the diverse characteristics of vibration signals, this paper first introduces a fault richness index based on the frequency-weighted energy operator to quantify fault information in signals. Subsequently, the complementary ensemble empirical mode decomposition ( CEEMD) technique is employed for signal denoising preprocessing, enhancing the signal reconstruction accuracy of the K-SVD algorithm in high-noise environments. We detail the application steps of CEEMD in practical signal processing and verify its denoising effect in high-noise environments through experimental data, further improving the signal reconstruction accuracy of the K-SVD algorithm. Additionally, this paper utilizes a sensitive component selection method based on the fault richness index to ensure that the recovered signal retains as much effective fault information as possible during the denoising process. Furthermore, the K-SVD algorithm is applied for secondary signal decomposition, and a novel dictionary initialization method is used to enhance the fault feature expression ability of dictionary atoms, thereby improving the algorithm′s operational efficiency and fault feature extraction accuracy. Finally, the effectiveness and accuracy of the proposed method are validated through simulations and experiments. The results indicate that the proposed method significantly enhances the accuracy and reliability of fault feature extraction, providing strong support for health monitoring and fault diagnosis of shipborne antenna transmission systems