Study on feature extraction and classification of ultrasonic flaw signals

One of the most important techniques of ultrasonic flaw classification is feature extraction of flaw signals, which directly affects the accuracy and reliability of flaw classification. Based on the non-stationary characteristic of ultrasonic flaw signals, a new feature extraction method of ultrasonic signals based on empirical mode decomposition (EMD) 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. According to specific characteristics of ultrasonic echo signal, identification defect diagnosis system for ultrasonic echo signal based on BP is built up, and the specific structure of BP neural network is designed. Finally BP neural network is made as decision-making classifier, signal eigenvector is inputted and flaw type is outputted. Experimental results show that the method has better performance in detecting ultrasonic flaw signals.