Multi-Sensor Tool Tear Yonitoring Combined with Temporal and Spatial Characteristics
In order to solve the problem that the traditional depth learning method for monitoring tool wear is tedious in extracting relevant features,and the incomplete extraction of data hidden information leads to low recognition accuracy,a multi-sensor tool wear monitoring model combined with spatio-temporal fea-tures is proposed.First,the waveform signals collected by different sensors are simply preprocessed as in-put,then the multi-channel 1D convolutional neural network(MC-1DCNN)is used to extract the spatial features of the input data,and then the bidirectional long short memory network(BiLSTM)is used to ex-tract the temporal features.Finally,the features are classified by the full connection layer and the Softmax layer.The simulation results show that the monitoring model has a simple process,high recognition accura-cy,and strong applicability.
NETL
NSTL
万方数据
维普
