Recognition of Tool Wear State Based on Inception-BiLSTM Under Few-Shot
Aiming at the difficulty of accurate fault diagnosis due to insufficient fault data in industrial pro-duction,the Inception-BiLSTM fault diagnosis method is proposed with the Inception module as the main structure and combined with the bidirectional long-short-term memory network(BiLSTM).The method is presented and validated experimentally with tool wear state recognition.First,the vibration signal is obtained by continuous wavelet transform(CWT)to obtain the time-frequency feature map,and the Inception net-work is used to extract the features of the time-frequency map;then,the feature vector is reduced in dimen-sion using global average pooling(GAP).Finally,BiLSTM is used to extract data information to identify tool wear status.The experimental results show that under the condition of small samples,the accuracy of this method is higher than that of the comparison method in identifying the tool wear state.
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