Development and validation of a strain-based Structural Health Monitoring system

An innovative Structural Health Monitoring (SHM) methodology, based on structural strain measurements, which are processed by a back-propagation feed-forward Artificial Neural Network (ANN), is proposed. The demonstration of the SHM methodology and the identification of its capabilities and drawbacks are performed by applying the method in the prediction of fatigue damage states of a typical aircraft cracked lap-joint structure. An ANN of suitable architecture is developed and trained by numerically generated strain data sets, which have been preprocessed by Fast Fourier Transformation (FFT) for the extraction of the Fourier Descriptors (FDs). The Finite Element (FE) substructuring technique is implemented in the stress and strain analysis of the lap-joint structure, due to its efficiency in the calculation of the numerous strain data, which are necessary for the ANN training. The trained network is successfully validated, as it is proven capable to accurately predict crack positions and lengths of a lap-joint structure, which is damaged by fatigue cracks of unknown location and extent. The proposed methodology is applicable to the identification of more complex types of damage or to other critical structural locations, as its basic concept is generic.

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