Fault diagnosis method and system based on standard self-learning data enhancement

The invention provides a fault diagnosis method and system based on standard self-learning data enhancement, and relates to the technical field of bearing fault diagnosis, and the method comprises the steps: constructing a fault diagnosis model based on a one-dimensional convolutional neural network; training the fault diagnosis model through a cross adversarial training mode of standard self-learning and data enhancement to obtain a complete data set and an intelligent fault diagnosis model under a strong non-stationary working condition; inputting the collected vibration signal to be diagnosed into the trained intelligent fault diagnosis model to obtain a bearing fault diagnosis result; according to the method, the one-dimensional convolutional neural network is taken as a basic framework, disturbance data is generated by using an incomplete training data set through a cross adversarial training mode of standard self-learning and data enhancement, a fault diagnosis model under a strong non-stationary workin