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.

With the extensive application of analog circuits in many electronic devices, it is important to achieve accurate alerts in the incipient fault stage of analog circuits to reduce the threat on their reliability. However, the faint nature of incipient faults and the tolerance of components lead identifying incipient faults as a huge research challenge. Consequently, a complex convolutional self-attention autoencoder (CCSAE) is proposed in this paper to perform incipient fault diagnosis for analog circuits, which contains a feature extraction module, a feature enhancement module, and a classification module. In the first module, a backbone based on the complex convolutional autoencoder (CCAE) is designed to provide effective feature representations containing the amplitude information and phase information of analog circuit responses. In the feature enhancement module, a complex self-attention layer is constructed to enhance the useful structural information for feature representations by capturing internal correlations, thus addressing the faint nature of incipient faults. Finally, a two-step training mechanism including feature training and classification training is designed for CCSAE, where the key operation is the construction of supervised contrast loss (SCL) to pull closer similar feature representations and push away dissimilar ones. To demonstrate the effectiveness and merits of the proposed method, a typical Sallen–Key bandpass filter circuit and an actual amplifier board circuit of the water jet propulsion device are considered as experimental circuits. The experimental results indicate that this method achieves an average accuracy of 99.92% in the former and 98.25% in the latter, which is superior to other excellent methods.

To solve the problem of nonlinear factors in the fault detection process of complex systems, this paper proposes a fault detection model based on vector quantization sparse autoencoder. First, a feature extraction model, which consists of a self-normalizing convolutional autoencoder module, a vector quantization module, a gradient module, and a loss module, is developed. Thereafter, a construction rule based on local Mahalanobis distance and K nearest neighbours is designed to calculate metrics that depend on the sparse features. A comprehensive statistic is constructed to accurately track the operating status of complex systems by combining the loss metric and the K Mahalanobis neighbour metric. Finally, the threshold of the fault detection statistics is determined by modelling the generalized extreme value distribution. Three case studies, a numerical simulation, the Tennessee Eastman benchmark process and a typical circuit system, are adopted to demonstrate the effectiveness and merits of the proposed model.