In recent years, the amount of household waste has increased sharply, and there is an urgent need to use intelligent waste classification equipment to assist in completing waste classification tasks. However, existing garbage classification algorithms have large parameter counts and poor real-time performance, which are not suitable for embedded garbage classification devices. Therefore, in order to achieve a lightweight and efficient classification model, this article uses a self-built garbage dataset and a pre-trained MobileNetV3 model in the PyTorch framework for garbage recognition and classification. We introduce the CBAM attention mechanism in the network model to enhance spatial feature perception. Utilize the Mish activation function to fully utilize the extracted depth image information. Using global average pooling instead of the fully connected layer of the original model reduces the number of model parameters while improving the recognition accuracy of the model. Finally, we propose an improved lightweight model called GMC-MobileNetV3. The experimental results show that the recognition accuracy of the improved MobileNetV3 model on self-built data set reaches 96.55%, which is 3.6% higher than that of the original model, the number of model parameters is 0.64M, the memory resource consumption is reduced by 56.6%, and the recognition time of a single garbage image is only 26.4ms. The network proposed in this article can achieve low consumption and high accuracy in garbage recognition and classification, providing reference for future academic research and engineering practice.

Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults is of great importance. Although existing deep learning fault diagnosis methods have achieved certain results, they still face limitations such as inadequate feature extraction capabilities, insufficient generalization to complex working conditions, and ineffective multi-scale feature capture. To address these issues, this paper proposes an advanced fault diagnosis method named the two-stream feature fusion convolutional neural network (TSFFResNet-Net). Firstly, the proposed method combines the advantages of one-dimensional convolutional neural networks (1D-ResNet) and two-dimensional convolutional neural networks (2D-ResNet). It transforms one-dimensional vibration signals into two-dimensional images through the empirical wavelet transform (EWT) method. Then, parallel convolutional kernels in 1D-ResNet and 2D-ResNet are used to extract multi-scale features, respectively. Next, the Convolutional Block Attention Module (CBAM) is introduced to enhance the network’s ability to capture key features by focusing on important features in specific channels or spatial areas. After feature fusion, CBAM is introduced again to further enhance the effect of feature fusion, ensuring that the features extracted by different network branches can be effectively integrated, ultimately providing more accurate input features for the classification task of the fully connected layer. The experimental results demonstrate that the proposed method outperforms other traditional methods and advanced convolutional neural network models on different datasets. Compared with convolutional neural network models such as LeNet-5, AlexNet, and ResNet, the proposed method achieves a significantly higher accuracy on the test set, with a stable accuracy of over 99%. Compared with other models, it shows better generalization and stability, effectively improving the overall performance of rolling bearing vibration signal fault diagnosis. The method provides an effective solution for the intelligent fault diagnosis of wind turbine rolling bearings.

The extraction of the vibration impulse signal plays a crucial role in the fault diagnosis of rolling element bearing. However, the detection of weak fault signals generally suffers the strong background noise. To solve this problem, a new adaptive multiscale enhanced combination gradient morphological filter (MECGMF) is proposed for the fault diagnosis of rolling element bearing. In this method, according to the filtering ability of four basic morphological filter operators, an enhanced combination gradient morphological operation (ECGMF) is first proposed. This design enhances the ability of MECGMF to extract impulse signals from strong background noise. And accordingly, a new adaptive selection strategy named kurtosis fault feature ratio (KFFR) is proposed to select an optimal structuring element (SE) scale. Subsequently, the optimal SE scale is the largest measure of multiscale morphological filtering for extracting bearing fault information. In the meanwhile, the effectiveness of the proposed method is verified by simulation and experiment. Finally, the experimental results demonstrate that MECGMF can effectively restrain the noise interference and extract fault characteristic signals of rolling element bearing from strong background noise. Moreover, comparative tests show that the proposed method is more effective in detecting wind turbine bearing failures.

As an important component of rotating machinery, the fault information of rolling element bearing is difficult to be recognized due to the background noise and harmonic frequency contained in the tested vibration signal. In order to accurately and completely extract the fault characteristic information from the vibration signal, a fault diagnosis research method (EAVGH-CSC-EES) based on the combination of enhanced top-hat morphological filtering (EAVGH) and cyclic spectrum coherence (CSC) is proposed. First of all, in view of the problem that the existing top-hat operators cannot fully extract the signal fault characteristics, this paper selects the optimal operator from the four enhanced morphology operators to construct the EAVGH. Since the reasonable selection of structural element (SE) scale has a great influence on the filtering result of morphological operators, then this paper applies feature energy factor (FEF) to select the optimal scale of SE. Subsequently, in order to further solve the influence of the non-linear modulation frequency components in the signal, while improving the filtering performance of EAVGH. This paper uses the cyclic spectrum coherence function (CSC) to further process the filtered signal. And then the enhanced envelope spectrum (EES) of the signal is obtained. Simulation and two sets of bearing fault experiments verify the rationality and effectiveness of the EAVGH-CSC method. The comparison results with other existing methods can further prove the superiority of the method proposed in this paper.

A theoretical research on eliminating the instability vibration and improving the stability of the rotor/seal system using the inerter‐based dynamic vibration absorber (IDVA) is presented in this paper. The modified Jeffcott rotor and Muszynska nonlinear seal force models are employed. The proposed IDVA is the damping element of the traditional dynamic vibration absorber (DVA) replaced by one of the six configurations of the inerter. The instability threshold speed of the system is obtained by applying the Routh–Hurwitz stability criterion. The quantum particle swarm optimization (QPSO) method is utilized to optimize the parameters of the IDVA. The numerical method is applied to investigate the nonlinear dynamic responses and stability. The results show that the IDVA can effectively improve the stability and reduce the instability vibration of the rotor/seal system. Furthermore, the performance of the IDVA is more effective than that of the traditional DVA.

Low-frequency noise absorbers often require large structural dimensions, constraining their development in practical applications. In order to improve space utilization, an acoustic metamaterial with a spatial double helix, called a spatial double helix resonator (SDHR), is proposed in this paper. An analytical model of the spatial double-helix resonator is established and verified by numerical simulations and impedance tube experiments. By comparing the acoustic absorption coefficients of the spatial double-helix resonator, it is shown that the results of the analytical model, the numerical model, and the experiments are in good agreement, proving the accuracy of the theoretical model. The effects of different structural parameters on the peak sound absorption coefficient and resonance frequency are quantitatively revealed. The impedance variation law of the model is obtained, and the resistance and reactance distributions at the resonance frequency are analyzed. In the optimization model, the Back Propagation (BP) network is used to construct the mapping between the structural parameters and the resonance frequency and sound absorption coefficient, and this is used as the constraints of the equation, which is combined with Wild Horse Optimization (WHO) to establish the BP-WHO optimization model to minimize the volume of the spatial double helix resonator. The results show that, for a given noise frequency, the optimized structural parameters enhance the space utilization without affecting the performance of the space double helix resonator.