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 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.

In this paper, a method is proposed to accurately measure the torsional vibration of a ship’s propulsion shafting, and calculate its instantaneous torque based on instantaneous speed data. The instantaneous speed signal of a ship is measured by a data acquisition instrument indirectly under the deceleration condition, and then filtered by the five-point smoothing algorithm. The filtered instantaneous speed signal is further processed to calculate the maximum amplitude of the torsional vibration, along with the maximum torque of the propulsion shafting under specific operating conditions. The proposed method can effectively improve the calculation accuracy of the torsional vibration of the shafting, and provides reliable theoretical basis for setting the safe speed of the ship’s diesel engine.