Estimation of the Dynamic Properties of Non-linear Packaging Materials Using a Reverse Multiple Input/Single Output Based Approach

Abstract

During the distribution phase, packaged consignments are exposed to a variety of environmental hazards (such as vibrations) that, if excessively severe, may cause damage to or even destroy the product. Structural deterioration can be tracked by monitoring variations in the packaging system's modal parameters, particularly its natural frequency (stiffness). Natural frequency estimates are often extracted using a least-squares regression curve fit, applied to an estimate of the system's frequency response function (FRF). FRF estimates are generally obtained using the Fourier transform with a single input and single output (SISO). This approach is suitable for many applications; however, as the non-linearity of the system under analysis increases, the ability of this technique to accurately monitor changes in the system will decrease. In addition, when the excitation to the non-linear system is varied (increased or decreased amplitude), a SISO-based approach may indicate a shift in natural frequency (as a result of the varied input) even though no change in the condition of the system has occurred. This paper discusses an approach that is designed to separate the linear component of the system's FRF using a reverse multiple input/single output (RMISO) algorithm. Such separation will allow traditional modal parameter extraction (curve fitting) techniques to be used to monitor the condition of non-linear systems. The paper presents the results of experiments in which expanded polystyrene samples were subjected to broad-band random base excitation with a free-moving load placed atop the cushion sample. Continuous acceleration measurements of the vibration table and the free-moving load were used to compute the FRFs of the cushions, and the differences between a conventional (SISO) approach and the proposed RMISO-based parameter extraction technique were evaluated. Copyright © 2014 John Wiley & Sons, Ltd.