Experimental decoupling of substructures by singular vector transformation

Abstract

Substructure decoupling is the process of identifying the dynamic behavior of one component by removing the dynamic influence of the second component from the assembled system. In experimental practice, several techniques have been developed to address the decoupling problem. In this context, measurements errors of random and systematic nature remain a major hindrance to a successful implementation of the methodology. For this reason, approaches such as extended interface, Virtual Point Transformation and truncated Singular Value Decomposition are commonly adopted on top of a standard interface decoupling procedure. This paper introduces the Singular Vector Transformation. The idea is to weaken the interface problem by using the Singular Value Decomposition to extract reduction spaces directly from the measured dynamics. A least square smoothing minimizes random errors and outliers, thereby improving the conditioning of the interface matrix inversion. No geometrical or analytical model is required. The reduction basis are frequency-dependent and can include flexible interface behavior, if properly controlled and observed. Further understanding and interpretation of the interface problem in frequency-based decoupling is provided. Numerical and experimental examples show the potential of the proposed technique in comparison with state-of-the-art approaches.