Bearing fault diagnosis method based on lightweight neural network and dimension expansion

The invention relates to the field of machine vision and fault diagnosis, in particular to a bearing fault diagnosis method based on a lightweight neural network and dimension expansion, and the method comprises the steps: obtaining historical bearing vibration signals, carrying out the normalization of the obtained bearing vibration signals, and carrying out the polar coordinate coding; on the basis of the Grubby angle sum field, the Grubby angle difference field and the Markov transition field, converting the bearing vibration signal subjected to polar coordinate coding into a two-dimensional bearing vibration signal; constructing a lightweight neural network, and training the neural network by using the two-dimensional bearing vibration signal; converting a to-be-detected bearing vibration signal into a two-dimensional bearing vibration signal, and inputting the two-dimensional bearing vibration signal into the trained lightweight neural network to obtain a diagnosis result; visualization of the vibration