Internal defect detection of magnetic tile based on CNN and modal transformation
Aiming at the demand for mature experience knowledge,unstable detection process and low efficiency in the processing of manual operation,an intelligent detection system is designed to avoid those drawbacks.Inspired by manual detection,we propose an internal defect detection method of magnetic tile based on convolution neural network(CNN)and modal transformation.The time domain signal is transformed into time-frequency domain spectrogram,and the convolution neural network is used to extract features and classify the spectrogram.In order to precisely emphasize important information and suppress irrelevant information,the coordinate attention mechanism is introduced into CNN.The accuracy of the prediction model based on convolution neural network and modal transformation achieves 98.4%,which proves that the proposed detection method is effective for the internal defect detection method of magnetic tile.The experimental results show that the modal transformation and coordinate attention mechanism can improve the performance of the model.
magnetic tileconvolutional neural network(CNN)internal defectmodal transformationattention mechanism
NETL
NSTL
万方数据
