Pawel Kostka

In situ integrity assessment of a smart structure based on the local material damping

Integration of functional elements into fibre-reinforced host structures provides the possibility for in situ monitoring of the structural integrity of critical components. In this study, a vibration-based monitoring function has been developed that allows the structural integrity identification of critical components. For this purpose, signal analysis algorithms were developed to enable the estimation of damage-dependent modal damping. The analysed smart structure was a carbon fibre–reinforced epoxy composite plate with an integrated actuating/sensing system. The local material damping is a parameter especially sensitive to different failure modes of composites. In order to characterise the changes of this parameter resulting from impact events, dynamical mechanical analysis on intact and damaged specimens made of the composite material was conducted. Based on the dynamical mechanical analysis results, a finite element model of the structure was developed. Then, modal damping ratios for different sizes and locations of damaged regions were numerically determined, and a relation between modal damping and damage-dependent local damping was identified. The deterministic decision trees describing the reverse relationship between online-measured modal damping and damage condition were determined. That was accomplished through the application of information entropy-based data-mining algorithms to the numerically generated learning dataset obtained using the developed finite element model.

A Simulation-Based Monitoring of a Composite Plate Using an Integrated Vibration Measurement System

The unique potential to integrate functional elements into fibre-reinforced components combined with the recent progress in the simulation models of composite materials provides new perspectives for reliability improvement of the next generation components. Such combination is presented on the example of a carbon-fibre reinforced composite plate with integrated vibration measurement and excitation systems. The investigated structure was consolidated in an adapted resin transfer moulding process using additional layers for positioning, contacting and isolating of the active elements. The integrated elements enable an online estimation of the structural dynamic behaviour and its damage-dependent changes.The article considers the identification problem of diagnostic models enabling a precise interpretation of the measured vibration responses. An approach based on the generation of classifiers by means of inductive machine learning algorithms is applied. At the baseline phase, modal properties are measured that correspond to the undamaged state of the structure. Using these experimental data, a simulation model of the structure was fitted by means of a mixed numerical experimental technique and used for the generation of multiple vibration patterns resulting from different mass distributions. The unique combination of experimental and numerical results enables a generation of high resolved learning datasets for machine learning algorithms using a minimum amount of experimental data. The verification of the estimated classifiers by means of the achievable diagnostic performance is firstly conducted theoretically using standardised validation techniques and a high performance is identified. Then, at the inspection phase, the performance of the whole diagnostic system is additionally experimentally confirmed based on the dynamic response resulting from different unseen structural disturbances.