Random dynamic load identification for stochastic structural-acoustic system using an adaptive regularization parameter and evidence theory. (14th April 2020)

• Record Type: Journal Article
• Authors: He, Z.C.; Zhang, Zhuomin; Li, Eric
• Is Part Of: Journal of sound and vibration. Volume 471(2020)
• Publication Date: 2020-04-14
• Subjects: random dynamic loads identification -- Structural-acoustic coupling -- Stochastic systems -- Improved regularization method -- Evidence theory; Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2020.115188
• Languages: English
• ISSNs: 0022-460X

Abstract

Abstract: In practical engineering, the non-contact measurement is more excellent due to preventing the potential damage of contact transducers. In this paper, a sound pressure-based method is proposed for identifying the random dynamic load acting on structural-acoustic coupled (SAC) system. Considering the characteristic of pseudo excitation and the influence rule of the regularization parameter, an adaptive regularization parameter is formulated to deal with the ill-posedness and reduce the error amplification of random excitation identification. Compared with the Tikhonov regularization method and Moore-Penrose pseudo inverse (MPI) method, the proposed improved regularization method reduces the identified error by 2.24 × 10 3 N 2 /Hz and 2.91 × 10 5 N 2 /Hz at 4 Hz and 6 Hz respectively in the studied example. The identified loads by improved regularization method match the actual loads adequately in the full frequency. Furthermore, owing to the uncertainty of the SAC system, the method for solving the stochastic system problem is investigated by the evidence theory combined with the interval analysis. Accordingly, the lower and upper bounds are derived to assess the statistical property of identified random dynamic loads. Finally, the effectiveness of the proposed methods is demonstrated by several numerical examples of complex stochastic systems.