Sparse regularization for force identification using dictionaries. (28th April 2016)

• Record Type: Journal Article
• Authors: Qiao, Baijie; Zhang, Xingwu; Wang, Chenxi; Zhang, Hang; Chen, Xuefeng
• Is Part Of: Journal of sound and vibration. Volume 368(2016)
• Page Start: 71
• Page End: 86
• Publication Date: 2016-04-28
• Subjects: Force identification -- Sparse regularization -- Tikhonov regularization -- Sparse reconstruction by separable approximation -- Dictionary; Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2016.01.030
• Languages: English
• ISSNs: 0022-460X

Abstract

Abstract: The classical function expansion method based on minimizing l 2 -norm of the response residual employs various basis functions to represent the unknown force. Its difficulty lies in determining the optimum number of basis functions. Considering the sparsity of force in the time domain or in other basis space, we develop a general sparse regularization method based on minimizing l 1 -norm of the coefficient vector of basis functions. The number of basis functions is adaptively determined by minimizing the number of nonzero components in the coefficient vector during the sparse regularization process. First, according to the profile of the unknown force, the dictionary composed of basis functions is determined. Second, a sparsity convex optimization model for force identification is constructed. Third, given the transfer function and the operational response, Sparse reconstruction by separable approximation (SpaRSA) is developed to solve the sparse regularization problem of force identification. Finally, experiments including identification of impact and harmonic forces are conducted on a cantilever thin plate structure to illustrate the effectiveness and applicability of SpaRSA. Besides the Dirac dictionary, other three sparse dictionaries including Db6 wavelets, Sym4 wavelets and cubic B-spline functions can also accurately identify both the single and double impact forces from highly noisy responses in a sparse representation frame. The discrete cosine functions can also successfully reconstruct the harmonic forces including the sinusoidal, square and triangular forces. Conversely, the traditional Tikhonov regularization method with the L-curve criterion fails to identify both the impact and harmonic forces in these cases. Highlights: Spare representation is extended to the field of force identification. SpaRSA is developed to solve l 1 regularization problem in force identification. The Dirac, Db6, Sym4 and B-spline dictionaries are used to represent impact force. The discrete cosine dictionary is used to represent three types of harmonic force. Compared with Tikhonov regularization, SpaRSA is highly accurate and efficient.