To enhance the reliability of analog circuits in complex electrical systems, a novel incipient fault diagnosis method is presented in this paper. The wavelet packet feature quantities, which consist of the energy, fluctuation coefficient, skewness and margin factor, are obtained via multiscale timefrequency analysis with wavelet packet transform (WPT). Then, generalized discriminant analysis (GDA) is employed to realize fusion of wavelet packet feature quantities because it can handle the data nonlinearity and eliminate redundant information. Furthermore, the generalized multiple kernel learning support vector machine (GMKL-SVM), which has the advantages of a strong generalization ability and high accuracy, is developed to identify the incipient fault classes of analog circuits. Moreover, a new particle swarm intelligent optimization algorithm, the sine cosine algorithm (SCA), is adopted to optimize key parameters of GMKL-SVM because of its high convergence speed and strong global optimization ability. The method is fully evaluated with the Sallen-Key bandpass filter circuit, the four-op-amp biquad highpass filter circuit and the leapfrog filter circuit. The experimental results demonstrate that the proposed incipient fault diagnosis method for analog circuits can produce higher diagnosis accuracy than other typical incipient fault diagnosis methods.