Using chaotic interrogation and attractor nonlinear cross-prediction error to detect fastener preload loss in an aluminum frame

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Abstract
Structural health monitoring is an important field concerned with assessing the current state (or “health”) of a structural system or component with regard to its ability to perform its intended function appropriately. One approach to this problem is identifying appropriate features obtained from time series vibration responses of the structure that change as structural degradation occurs. In this work, we present a novel technique adapted from the nonlinear time series prediction community whereby the structure is excited by an applied chaotic waveform, and predictive maps built between structural response attractors are used as the feature space. The structural response is measured at several points on the structure, and pairs of attractors are used to predict each other. As the dynamics of the structure change due to damage, the prediction error rises. This approach is applied to detecting the preload loss in a bolted joint in an aluminum frame structure.