The invention discloses a
machine tool cutter residual life prediction method based on LSTM + CNN, and the method comprises the steps: carrying out the judgment of the
signal features of uploaded training data, and distinguishing a
continuous signal and a discrete
signal; performing data merging on the real-
time data of different frequencies sampled by the sensor; checking whether missing values or abnormal values exist in the training data and the real-
time data or not; if the missing values or the abnormal values exist, using a
moving average method to supplement the missing values or replacing the abnormal values, so as to enable the data to be complete and effective, and removing outliers; carrying out selection and dimension reduction on the training data and the real-
time data according to data characteristics so as to facilitate
model fitting and prevent an over-fitting phenomenon; and training and testing the LSTM + CNN model, and adjusting training parameters and
model parameters according to the error, so as to reduce the error to a reasonable range. According to the method, the precision of the prediction result is improved by adopting a grouping mode and a dimension reduction mode, deterministic factors and uncertain factors are comprehensively considered, and the precision of the prediction result can be effectively improved.