Abstract

The paper presents work related to nonlinear system parameters identification. The research is focused on systems with hysteretic stiffness characteristics. The identification procedure is developed with use of artificial neural networks. The presented method assumes two separate clusters of neural networks, which are supported by additional signal processing block. Such approach gives an advantage over the conventional identification methods due to its small restrictions. The validation process considers structural responses in time and frequency domains as well as the restoring force plane of the dynamic structure. First, verification of the identification method is performed on the numerical simulation of the system with hysteretic stiffness. Next, the identification of the real dynamic system with contact-related nonlinearity is carried out. The steel samples with contacting surfaces were used in the experiment. Electromagnetic shaker was used to excite the structure and enforce a relative shear motion between surfaces in contact. The system response was recorded using the Polytec laser vibrometer.

中文翻译：

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使用人工神经网络识别接触表面之间的粘滞和滑动运动

摘要

本文介绍了与非线性系统参数识别有关的工作。研究集中在具有滞后刚度特性的系统上。识别程序是使用人工神经网络开发的。所提出的方法假定了两个独立的神经网络簇，这些簇由额外的信号处理模块支持。由于其局限性小，因此与常规识别方法相比具有优势。验证过程考虑时域和频域的结构响应以及动态结构的恢复力平面。首先，对具有滞后刚度的系统的数值模拟进行识别方法的验证。接下来，对具有接触相关非线性的实际动态系统进行识别。在实验中使用了具有接触表面的钢样品。电磁振动器用于激发结构并在接触的表面之间强制进行相对剪切运动。使用Polytec激光测振仪记录系统响应。