A Modeling and Classification Method of Ultrasonic Signals Based on Empirical Mode Decomposition and Neural Network

A new modeling and classification method of ultrasonic signals based on empirical mode decomposition(EMD) and neural network is put forward in the paper. Firstly, the original ultrasonic flaw signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs) by EMD, and the Fourier transformation of IMF is made. The next step is to find a set of classification values from time domain and frequency domain of IMFs relating to flaw information, and to analyze these classification values and construct vector as signal eigenvector for identification. Finally make BP neural network as diagnoses decisionmaking classifier, input signal eigenvector, output flaw type. Experimental results show that the method has better performance in detecting ultrasonic flaw signals. Key-Words: ultrasonic signal; empirical mode decomposition(EMD); intrinsic mode function; modeling; neural network; eigenvector