Detection of system changes due to damage using a tuned hyperchaotic probe

This study explores the use of a hyperchaotic signal as an excitation to probe a system for dynamic changes induced by damage events. In chaotic interrogation a deterministic chaotic input (rather than the more commonly employed stochastic white noise input) is applied to the structure and the dynamic response is mined for features derived from its state space reconstruction. The steady-state chaotic excitation is tuned to excite the structure in a way that optimal sensitivity to dimensionality changes in the response may be observed, resulting in damage-sensitive features extracted from the resulting attractors. The enhanced technique proposed in this paper explores a hyperchaotic excitation, which is fundamentally new in its use as an excitation. Hyperchaotic oscillators have at least two Lyapunov exponents, in contrast to simple chaotic oscillators. By using the Kaplan-Yorke conjecture and performing a parametric investigation, the steady-state hyperchaotic excitation is tuned to excite the structure in such a way that the optimal (as will be defined) dimensionality of the steady-state response is achieved. A feature called the 'average local attractor variance ratio' (ALAVR), which is based on attractor geometry, is used to compare the geometry of a baseline attractor to a test attractor. The enhanced technique is applied to analytically and experimentally analyze the response of an eight-degree-of-freedom system to the hyperchaotic excitation for the purpose of damage assessment. A comparison between the results obtained from current hyperchaotic excitation versus a chaotic excitation highlights the higher damage sensitivity in the system response to the hyperchaotic excitation.