Machine tool milling cutter state recognition method based on multi-source heterogeneous data fusion

The invention relates to a machine tool milling cutter state recognition method based on multi-source heterogeneous data fusion, and belongs to the technical field of numerical control machine tool cutter wear monitoring. The method comprises the following steps: a data preprocessing method based on compressed sensing and noise adding processing; a stack sparse auto-encoder based on a Dropout method; a data fusion algorithm based on an improved D-S evidence theory; a tool state monitoring model based on SSAE and an improved D-S evidence theory. The practicability of the method provided by the invention is verified, and the production line state data is obtained in an automatic monitoring platform of the production line for test verification. According to the method, the problems of over-fitting of a deep learning network and low recognition precision of small-sample multi-source heterogeneous data are solved. Compared with an artificial feature extraction method and an SSAE method, the method has better ident