An analog circuit soft fault diagnosis method based on Boruta feature selection and LightGBM model

Analog circuits are an important part of modern electronic power systems. Accurate detection of analog circuit faults, especially soft faults, is of great significance for the maintenance and inspection of electronic systems. This paper proposes to apply the Boruta feature selection method to the field of soft fault diagnosis of analog circuits to screen out lowdimensional and efficient feature components from the high-dimensional time-domain statistical features and frequencydomain features of circuit responses. Then, the feature components are used as input to train the LightGBM classification model, and the Bayesian optimization method is introduced to optimize the model hyperparameters. Finally, the trained fault diagnosis model is verified in two typical experimental circuits, and satisfactory accuracy is obtained.