Zhousuo Zhang

This study proposes a new finite element modeling approach for rough surface contact by incorporating the material plastic hardening and non-Gaussian distribution characteristics of rough surfaces, providing an alternative to actual surface analysis. Firstly, a four-parameter nonlinear hardening function is refined to model the plastic hardening behavior of the material, followed by the derivation of the incremental form of the implicit integration method for the elastic-plastic constitutive equation. Secondly, a finite element model for rough surface contact analysis is constructed based on the isotropic hardening criterion. The non-Gaussian rough surface is generated through a Fast Fourier Transform (FFT) surface reconstruction method combined with the Johnson translator system. Finally, the proposed model is utilized to analyze the effects of plastic hardening and non-Gaussian surface parameters on the rough surface contact behavior. The results demonstrate that the proposed model effectively captures the plastic hardening characteristics. Furthermore, the analysis revealed that under identical normal loads, rough surfaces with negative skewness and platykurtosis exhibited better load-bearing capacities. This study provides a foundation for the analysis of the contact characteristics in connection structures.

Mode mixing is a common phenomenon in the propagation of ultrasonic guided waves, which may be caused by inherent multi-mode and medium scattering. In the process of state detection using guided waves, mode mixing may greatly reduce accuracy of information extraction, and a single mode can often achieve better characterization. Therefore, for the mode separation problem of guided wave signals, this article proposes an improved sparse norm method to identify actual modes more effectively. Simulational and experimental results show that the proposed method can achieve a better sparse representation of guided wave signals under certain conditions, compared to the latest methods.

Online identification of satellite micro-vibration sources can provide a basis for online suppression of satellite micro-vibration, which has significant application value for improving the positioning accuracy and resolution of satellite. The complex time-varying environments in orbit, high real-time requirements and numerous parameters to be estimated pose great challenges to the adaptability, efficiency and accuracy of online source identification algorithms. Aiming at the challenges for online satellite source identification, a novel online sliding blind deconvolution algorithm is proposed. Firstly, blind deconvolution is converted online to the instantaneous blind source separation (BSS) in the selected single frequency bin by sliding discrete Fourier transform (SDFT), which greatly reduces the computation and improves the efficiency of online separation. Secondly, my previous work, namely adaptive step size EASI algorithm based on nonlinear correlation (NC-EASI algorithm), is extended to the complex-domain to achieve the online separation. Finally, the separated signals in time-domain are obtained by performing the inverse SDFT. The effectiveness of the proposed algorithm under time-varying environment is validated by several numerical simulations and the excitation experiments of aluminum honeycomb panel cabin structure. Compared with the comparison algorithms, the separation accuracy of simulation and experiment in time-varying environments has been improved by an average of 4.295dB and 6.64dB, respectively, and the convergence time has been shortened by an average of 44.42% and 65.11%. The proposed algorithm can provide an effective means for the online source separation of complex mechanical systems.