Pattern recognition of guided waves for damage evaluation in bars

Abstract

Guided waves damage identification in bars with neural networks acquires training data from simulation as a cost-effective measure. These neural networks applied with a novel test inputs dependent iterative training scheme are capable of quantifying damages accurately from experimental inputs. The reliability of the predictions depends on the quality of the measured signals, which can be increased by considering more than one signal obtained from different sensor locations or by changing the properties of the interrogation pulse. A parallel network system to process the inputs from these signals collaboratively is described. The core of the system is a data fusion process that associates overlapping intermediate test results while isolating outliers to narrow the training range for improved generalization in the iterative test inputs dependent training scheme. This robust system of signal processing has achieved accurate average damage quantitative results with errors below 4% and 13% the original size of the training parameter space for damage location and depth, respectively, of artificial laminar defects in bars.