A novel approach for classification of loads on plate structures using artificial neural networks

• ANN was employed to classify the applied load location on plates.
• High-frequency surface guided waves were utilized by SuRE method and spectrum data was collected.
• MLP and RBF classifiers were used and load was applied on aluminum and composite plates.
• Measurements were conducted using laboratory equipment and a low cost DSP circuit.
• ANNs were trained by sums of square of differences (SSDs) of load spectrums from no-load baseline.

In this study the location of applied load on an aluminum and a composite plate was identified using two type of neural network classifiers. Surface Response to the Excitation (SuRE) method was used to excite and monitor the elastic guided waves on plates. The characteristic behavior of plates with and without load was obtained. The experiments were conducted using two set of equipment. First, laboratory equipment with a signal generator and a data acquisition card. Then same test was conducted with a low cost Digital Signal Processor (DSP) system. With experimental data, Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural network classifiers were used comparatively to detect the presence and location of load on both plates. The study indicated that the Neural Networks is reliable for data analysis and load diagnostic and using measurements from both laboratory equipment and low cost DSP.