The invention discloses an airborne
radio frequency system filter circuit fault diagnosis method based on confidence fusion, and belongs to the technical field of analog circuit fault diagnosis. The method comprises the following steps: firstly, acquiring an output end
voltage data set in a circuit health state and each fault state, converting
time domain data into
frequency domain data by using FFT (
Fast Fourier Transform), then carrying out logarithmic compression, and normalizing the
time domain data; constructing a 1D-CNN model, and performing fault diagnosis by using the
time domain data to obtain a time domain
probability vector; constructing a Transform model, and performing fault diagnosis by using the
frequency domain data to obtain a
frequency domain probability vector; a fusion
probability vector is obtained through weighted fusion, and a fault category corresponding to the maximum probability in the fusion probability vector is selected as a diagnosis result; training by using the
training set, and verifying validity by using the
test set. According to the method, time-frequency double-domain
feature extraction is adopted to capture multi-dimensional representation of a circuit state; in combination with 1D-CNN local
feature extraction and Transform global association, computing
resource consumption is reduced, and the accuracy and robustness of a diagnosis result are greatly improved.