Method for diagnosing state of rolling bearing based on self-attention neural network

The invention discloses a method for diagnosing a state of a rolling bearing based on a self-attention neural network, comprising the steps of: using a vibration acceleration sensor to acquire timingsequence signals of vibration acceleration of the rolling bearing in different states under different motor loads to obtain vibration acceleration data to be used as sample data; performing data enhancement processing on the acquired sample data; attaching corresponding labels to the sample data subjected to the data enhancement processing according to the type of the state of the rolling bearing;extending characteristic vectors of each sample data by using multilayer mapping, so as to change one-dimensional characteristics into multi-dimensional characteristics; establishing a self-attentionnetwork diagnosis model; training the self-attention network diagnosis model by using the processed sample data, evaluating the trained self-attention network diagnosis model, and applying the trained self-attention network di