Damage detection in an uncertain nonlinear beam based on stochastic Volterra series: an experimental application

TL;DR

This paper demonstrates an experimental method for damage detection in nonlinear structures using a stochastic Volterra series, effectively distinguishing damage from natural data variations and outperforming deterministic models.

Contribution

It introduces a stochastic Volterra series approach combined with novelty detection for damage identification in nonlinear systems considering data uncertainties.

Findings

Stochastic model outperforms deterministic in damage detection.

Nonlinear metrics show higher sensitivity to damage.

Method successfully detects damage despite data variability.

Abstract

The damage detection problem becomes a more difficult task when the intrinsically nonlinear behavior of the structures and the natural data variation are considered in the analysis because both phenomena can be confused with damage if linear and deterministic approaches are implemented. Therefore, this work aims the experimental application of a stochastic version of the Volterra series combined with a novelty detection approach to detect damage in an initially nonlinear system taking into account the measured data variation, caused by the presence of uncertainties. The experimental setup is composed by a cantilever beam operating in a nonlinear regime of motion, even in the healthy condition, induced by the presence of a magnet near to the free extremity. The damage associated with mass changes in a bolted connection (nuts loosed) is detected based on the comparison between linear and nonlinear contributions of the stochastic Volterra kernels in the total response, estimated in the reference and damaged conditions. The experimental measurements were performed on different days to add natural variation to the data measured. The results obtained through the stochastic proposed approach are compared with those obtained by the deterministic version of the Volterra series, showing the advantage of the stochastic model use when we consider the experimental data variation with the capability to detect the presence of the damage with statistical confidence. Besides, the nonlinear metric used presented a higher sensitivity to the occurrence of the damage compared with the linear one, justifying the application of a nonlinear metric when the system exhibits intrinsically nonlinear behavior.