Focusing on the remaining useful life prediction of cutting tool

a method combining one-dimensional convolutional auto encoder (1DCAE) with residual bidirectional gated recurrent unit (RBGRU) is proposed. The underlying features of the working condition data are obtained by using 1DCAE continuous convolution pooling and deconvolution upsampling methods

and fused with the segmented original signal. And the fused data is used to characterize the remaining life of tool. Meanwhile

the structure of the bidirectional gated recurrent unit (BiGRU) is improved by combining the idea of residual network to enhance the capability of capturing the timing features. The experimental results show that the prediction accuracy of the present method is better than that via the other algorithms.