Mechanical fault diagnosis method for unsupervised deep learning network

The invention discloses a mechanical fault diagnosis method for an unsupervised deep learning network. The method comprises the steps of: (1) mounting corresponding sensors near the part such as bearing of a mechanical device to collect mechanical vibration signals; (2) converting the collected vibration signals into a mixed domain fault feature data set, and dividing the data set into a testing and a training sample feature subset; (3) inputting the training sample feature subset into the constructed unsupervised deep learning network (UDLN) model for learning and training, wherein the UDLN model is composed of two improved sparse filtering (L12SF) unsupervised feature extraction layers and one weighted Euclidean distance similarity affine (WE) clustering layer; (4) inputting the test sample into a trained diagnosis model to realize full-range unsupervised feature learning and fault clustering; and (5) calculating the recognition rate of the test sample clustering division according to the membership degree