利用子空间法识别统计能量分析参数

IDENTIFICATION OF STATISTICAL ENERGY ANALYSIS PARAMETERS USING SUBSPACE METHOD

利用统计能量分析进行高频动力学环境响应预示的关键步骤之一是确定可靠的统计能量分析参数。该文研究了基于子空间的统计能量分析一阶功率流模型辨识和模型修正理论,基于多变量输出误差状态空间(MOESP)算法和约束优化思想,提出一种利用子空间法识别统计能量分析参数的新方法。通过算例对该方法进行了仿真验证,结果表明方法可行并具有较好的抗噪性能。最后,对L型板结构进行了实验统计能量分析,比较了子空间方法和功率输入法结果,两者吻合很好,从而进一步验证了子空间方法的正确性。该文验证了利用瞬态时域数据进行统计能量分析模型修正和参数识别的可行性,也是对实验统计能量分析的补充和发展。

The reliable estimate of statistical energy analysis （SEA） parameters is one of the most important steps in a SEA prediction work for high frequency vibration response. The theory of the first order power flow model identification as well as the SEA model improvement is studied. A method for SEA parameters identification using subspace method is proposed, which is on the basis of the multi-variable output error state space （MOESP） algorithm and constrained optimization theory. The numerical simulation result demonstrates that the proposed method is feasible and has good anti-disturbance performance. Also experimental SEA on a L-shaped plate structure is processed, the results of a subspace and a power input method are in agreement, which further verify the former. The present study manifests that SEA model update and parameter identification can be carded out by using transient vibration data in time domain, which provides a good complement to experimental SEA.