A Novel General Feature Enhancement Method Based on Genetic Programming for Improving RF Circuit Fault Diagnosis Using Machine Learning - Wu, Kunping; Long, Bing; Bu, Zhiyuan; Wang, Jingyuan; Liu, Zhen (Circuits, systems, and signal processing, 2025)
Radio frequency (RF) circuits play a crucial role in numerous fields such as communication, radar, and navigation. However, due to their high operating frequencies, they are prone to failures under the influence of environmental factors and parasitic parameters. Existing methods for diagnosing RF circuit faults are mainly based on deep learning approaches. But the limited number of internal measurement points and the large variety of fault patterns result in complex network structure design and difficulties in application. In this manuscript, a novel general feature enhancement method based on genetic programming (GP) is proposed to improve the machine learning-based RF circuit fault diagnosis. Firstly, the time-frequency analysis of the fault signal is carried out based on the Variable Mode Decomposition-Hilbert (VMD-Hilbert) transform to obtain the original feature set. Then, the feature reconstruction method based on GP is used to achieve feature enhancement. Finally, the enhanced features are combined with machine learning algorithms to realize the fault diagnosis of RF circuits. Taking the experiment of a low-noise amplifier circuit as an example, after adopting the feature enhancement method in this manuscript, the diagnostic accuracies of Support Vector Machine and Naive Bayes are increased by 10.48% and 10.21% respectively. The experimental results demonstrate that this feature enhancement method can significantly improve the accuracy and stability of RF circuit fault diagnosis. Moreover, this feature enhancement method can be combined with any machine learning algorithm and applied to the fields of fault diagnosis, prediction, etc. of other systems.