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摘要
This article presented a novel signal processing and defect recognition method in MFL inspection system. During the preprocessing course, time-frequency analysis, median and adaptive filter, and interpolation processing are adopted to preprocess MFL inspection signal. In order to obtain high sensitivity and precision, we adopted multi-sensor data fusion technique to inspection data. A wavelet basis WBF) neural network was used to recognize defect parameters. Through constructing a knowledge-based off-line inspection expert system, the system improved its defect recognition capability greatly.
