Tool wear recognition and signal labeling with small cross-labeled samples in impeller machining

Data-driven deep learning method is the main way to study the condition monitoring of mechanical equipment, in which sufficient labeled signals to train the model parameters is a typical problem. The existing methods to obtain the labeled signals mainly focus on manual marking. For the non-batch impeller processing with variable working conditions, manually marking signals is not the wisest move. To solve this problem, this manuscript puts forward a deep conditional random field neural network (CRFNN) method. This framework fully utilizes the sensitivity of the conditional probability model to adjacent data marker information, and small cross-labeled samples are used to predict the labels of unknown signals. At the same time, the variational autoencoder is used to convert the one-dimensional time series signal into a three-dimensional image, which solves the problem that the empty tool signals have a great impact on the tool wear condition monitoring in the process of impeller blade machining. Experimental results on a CNC machining center demonstrate the effectiveness and feasibility of the proposed method and outperform the existing works under industrial small labeled samples.