Viscoelastic damping materials are an effective means to control structural vibration, and are widely used in various fields. In this paper, we use the Dynamic Mechanical Analysis (DMA) characterization data of viscoelastic damping materials and dynamic characteristics experiments to study the dynamic characteristics of structural damping, analyze and summarize the relationship between the performance of damping materials with temperature and frequency, and explore the influencing factors of damping materials on structural vibration. The research shows that temperature and frequency have great influence on the performance of damping material, and the storage modulus and loss factor change regularly. The modal experiment analysis verifies that the viscoelastic damping material has a good suppression effect on structural vibration, which provides a theoretical basis for the development of damping materials with controllable temperature and frequency domains.

In this paper, a quadratic convolution neural network (QCNN) using both audio and vibration signals is utilized for bearing fault diagnosis. Specifically, to make use of multi-modal information for bearing fault diagnosis, the audio and vibration signals are first fused together using a 1 × 1 convolution. Then, a quadratic convolution neural network is applied for the fusion feature extraction. Finally, a decision module is designed for fault classification. The proposed method utilizes the complementary information of audio and vibration signals, and is insensitive to noise. The experimental results show that the accuracy of the proposed method can achieve high accuracies for both single and multiple bearing fault diagnosis in the noisy situations. Moreover, the combination of two-modal data helps improve the performance under all conditions.

In this paper, a graph convolutional network is constructed and applied for bearing fault diagnosis. Specifically, the constant-Q transform (CQT) is first adopted for spectral analysis of vibration signals, where the frequencies are distributed in the logarithmic scale. Varied frequency resolutions can be obtained to satisfy the spectral resolution requirement and reduce signal dimension. Afterwards, the CQT spectrum is modeled by a graph, where nodes are frequency bins and edges reflect the inner relationship of different bins. There are edges between the fundamental and harmonic components. Then, a two-layer graph convolutional network (GCN) is utilized to assess the significance of vibration sources within the mixed signals. Finally, the bearing faults are determined according to the output of the GCN. To the best of our knowledge, this is the first work to model the vibration signal in this graph structure. The advantage of this approach lies in the simplification of edge definitions, facilitating shared connectivity relationships between the fundamental frequency and harmonics. Its performance was compared with another state-of-the-art fault diagnosis model. Experimental results demonstrate that the proposed model obtains higher accuracy, and it is more effective in extracting discriminative features.

Damage localization in GFRP (glass-fiber-reinforced polymer) composite plates is a crucial research area in marine engineering. This study introduces a feedback-based damage index (DI) combined with multi-label classification to enhance the accuracy of damage localization and address scenarios involving multiple damages. The research begins with the creation of a modal database for yachts’ GFRP composite plates using finite element modeling (FEM). A method for deriving a feedback-weighted matrix, based on the accuracy of the DI, is then developed. Sensitivity analysis reveals that the feedback DI is 50% more sensitive than the traditional DI, reducing false positives and missed detections. The associated feedback-weighted matrix depends solely on the structural shape, ensuring its transferability. To address the challenge for localizing multiple damages, a multi-label classification approach is proposed. The synergy between the feedback optimization and multi-label classification enables the rapid and precise localization of multiple damages in GFRP composite plates. Modal testing on damaged GFRP plates confirms the enhanced accuracy for combining the feedback DI with multi-label classification for pinpointing damage locations. Compared with traditional methods, this feedback DI method improves sensitivity, while multi-label classification effectively handles multiple damage scenarios, enhancing the overall efficiency of the damage diagnosis. The effectiveness of the proposed methods is validated through experimentation, offering robust theoretical support for composite plate damage diagnostics.

忆阻网络是一种基于忆阻器单元的大规模非线性电路, 在下一代人工智能、生物电子、高性能存储器等新兴研究领域发挥着重要作用. 描述忆阻器单元物理和电学特性的模型对忆阻网络的性能仿真具有显著影响. 然而, 现有模型主要为非解析模型, 应用于忆阻网络分析时可能存在收敛性问题. 因此, 提出了一种基于同伦分析法(homotopy analysis method, HAM)的忆阻器单元解析建模策略, 该策略具有解析性和收敛性优化的特点, 可提高忆阻器单元和相应忆阻网络的收敛性. 此外, 还提出了一种面向忆阻器单元模型的验证准则, 以验证模型在大规模忆阻网络中的适用性. 通过忆阻器单元和忆阻矩阵网络的长时演化实验以及与传统非解析(数值)方法的比较, 验证了所提策略的解析性和收敛性优势; 利用不同类型忆阻器单元和输入的实验, 验证了该策略的扩展性. 进一步地, 基于上述实验, 揭示了忆阻网络仿真出现收敛性问题的潜在原因. 该策略具有潜力应用于基于忆阻网络的新兴研究.

For application of non-metallic material in efficient vibration isolation, the polymer materials are used to construct a ST (sleeve type) isolator. The outside parts of ST isolator are made of PLA material, and the inside parts of ST isolator are made of TPU material. The polymer material model is linear elastic and isotropic. The numerical model of the ST isolator is calculated by using the finite element method. The static analysis and vibration isolation performance are analyzed. The numerical results show that when the ST isolator is under a force of 20000 N in vertical direction, the maximun deformation is 0.80 mm and the maximum stress is 2.68×10⁷ Pa. When under a force of 5000 N in horizontal direction, the maximun deformation is 1.44 mm and the maximum stress is 3.70×10⁷ Pa. The natural frequency of ST isolator is higher than 1800 Hz, and the fixed frequency of ST isolator is higher than 560 Hz. The harmonic analysis and transient analysis results show that the ST isolator has an efficient vibration isolation performance, especially when the ST isolator is fixed on a rigid foundation. This 3D printed ST isolator can be used in next engineering application.