Sparse deconvolution method for impact load identification of mechanical structure

Abstract

The invention relates to a sparse deconvolution method for impact load identification of a mechanical structure. The method is used for solving the ill-posed nature of the impact load identification inverse problem and comprises steps as follows: 1) a frequency response function between an impact load acting point and a response measurement point of the mechanical structure is measured with a hammering method, a unit impulse response function is obtained through inverse fast fourier transform, discretization is further performed, and a transfer matrix is obtained; 2) an acceleration signal generated by impact load of the mechanical structure is measured with an acceleration sensor; 3) an L1-norm-based sparse deconvolution convex optimization model for impact load identification is established; 4) the sparse deconvolution optimization model is resolved with a primal-dual interior point method, and a sparse deconvolution solution, namely, a to-be-identified impact load, is obtained. The sparse deconvolution method is suitable for identifying the impact load acting on the mechanical structure. Compared with conventional Tikhonov regularization methods based on an L2 norm, the sparse deconvolution method has the advantages of high identification accuracy, high computation efficiency and high stability.

Description

technical field [0001] The invention belongs to the field of state monitoring of mechanical systems, and in particular relates to a sparse deconvolution method for identifying impact loads of mechanical structures. Background technique [0002] Impact loads are a very important class of dynamic loads, especially in structural health monitoring of composite materials. For example, fan blades used for wind power generation are inevitably impacted by foreign objects such as wind and sand, flying birds, hail, and maintenance tools during operation and maintenance, and the frequency of occurrence is relatively high. Potential safety hazards caused by the stability and carrying capacity; the sudden airflow acting on the wings of the aircraft, the impact of birds and maintenance tools on the wings. It is very necessary to monitor such shock signals in real time, and it is very difficult to measure them directly. [0003] Impact load identification methods such as classic Tikhonov...

AI Technical Summary

Problems solved by technology

This method achieves the purpose of regularization and approximation to dynamic loads by controlling the number of basis functions. The biggest difficulty in its application lies in its regularization parameters—the number of basis functions is difficult to determine. Too many or too few basis functions will lead to invalid results; However, the neural network method around impact load identification requires a large number of samples to learn various impact events, which is very limited in practical engineering applications.

The traditional regularization method is difficult to deal with continuous impact load identification under large data scale. At this time, the condition number of the transfer matrix is ​​very large, and the numerical stability is very poor. In addition, when the response noise level is high, the performance of the traditional regularization method decreases, or even powerless

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