The invention discloses a bearing fault diagnosis method integrated with a support vector
dynamic neural network, and the method comprises the following steps: obtaining vibration data of a key position of a bearing of a rotating
machine, carrying out the data preprocessing of the vibration data, and obtaining a preprocessed vibration
signal data set; constructing a
support vector machine model with a quadratic
programming problem form based on the vibration
signal data set, and designing a corresponding penalty function and a Lagrange function to simplify the
support vector machine model; designing a variable parameter dynamics solving equation, and establishing a final integrated support vector
dynamic neural network bearing fault diagnosis model to solve the
support vector machine model; and simultaneously training a plurality of support vector
dynamic neural network bearing fault diagnosis models based on a bagging classifier method, carrying out bearing state diagnosis on the vibration
signal data set, and outputting a diagnosis result. According to the invention, the accuracy of the
machine learning method, the convergence proof of the neural network
solver and the
interpretability of the intelligent fault diagnosis model are improved.