Neural Network Based Classifier for Ultrasonic Resonance Spectra

In this paper we present a neural network based spectrum classifier (NSC) and its application to ultrasonic resonance spectroscopy. The use of an Artificial Neural Network (ANN) is proposed to meet the requirements of high sensitivity for small but relevant changes in the spectra, and simultaneous robustness against measurement noise. Provided with enough training examples, the ANNs are known to be able to find features representative for different classes and to generalize in order to cope with the measurement noise. Among several types of ANNs that could be used for classifying the spectra we have chosen a multi-layer perceptron (MLP). Although the MLP itself can perform feature extraction, we included an optional pre-processor for this purpose. The NSC is essentially model free and can be trained using real and modeled spectra. The classifier uses both amplitude and phase information in the spectra. The performance of the classifier has been verified using a number of practical applications, here we present results of its application to detection of disbonds in adhesively joint multi-layer aerospace structures using Fokker Bond Tester resonance instrument. In this case the classifier is capable of detecting very small disbonds (larger than 25% of the sensor area) and correct identifying their position in the structure (identifying the defected joint).