Rolling bearing fault diagnosis method based on 1D-CNN

The invention discloses a rolling bearing fault diagnosis method based on a 1D-CNN. The method comprises the following steps: S1, respectively obtaining an inner ring fault, an outer ring fault, a rolling body fault and vibration signal data in a normal state of rolling bearing equipment; S2, performing preprocessing operation on the vibration signal data, including intercepting the four types ofvibration signal data to obtain four types of samples required by the model, labeling the four types of samples respectively, and dividing the labeled samples into a training set and a verification set; S3, establishing a 1D-CNN initial model, and training the initial model by using the preprocessed training set to obtain a rolling bearing fault diagnosis model; checking the diagnosis performanceof the rolling bearing fault diagnosis model by using the verification set; and S4, acquiring a vibration signal of the rolling bearing equipment in real time, intercepting the vibration signal to obtain a sample, and inputtin