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.

Multi-channel active noise control (ANC) is currently an attractive solution for the attenuation of low-frequency noise fields, in three-dimensional space. This paper develops a controller for the case when the noise source components are sparsely distributed in space. The anti-noise signals are designed as in conventional ANC to minimize the residual errors but with an additional term containing an ℓl norm regularization applied to the signal magnitude. This results in that only secondary sources close to the noise sources are required to be active for cancellation of sparse noise fields. Adaptive algorithms with low computational complexity and faster convergence speeds are proposed.

Fire is one of the most serious disasters in the wild environment such as mountains and jungles, which not only causes huge property damage, but also may lead to the destruction of natural ecosystems and a series of other environmental problems. Considering the superiority and rapid development of computer vision, we present a novel intelligent wildfire detection method through video cameras for preventing wildfire hazards from becoming out of control. The model is improved based on YOLOV5S architectures. At first, we realize its lightweight design by incorporating the MobilenetV3 structure. Moreover, the improvement of detection accuracy is achieved by further improving its backbone, neck, and head layers. The experiments on a dataset containing a large number of wild flame and wild smoke images have demonstrated that the novel model is suitable for wildfire detection with excellent detection accuracy while meeting the requirements of real-time detection. Its wild deployment will help detect fire at the very early stage, effectively prevent the spread of wildfires, and therefore significantly contribute to loss prevention.