A bearing fault diagnosis method based on adaptive manifold probability distribution, comprising the following steps: constructing a plurality of migratable domains and migration tasks; then converting a data sample in each migration task into frequency domain data using a Fourier transform, inputting the frequency domain data into a GFK algorithm model, and calculating, using the GFK algorithm model, a manifold feature representation matrix in each migration task associated with a bearing fault; according to the manifold feature representation matrix, calculating a cosine distance between the center of a target domain and the center of a source domain in each migration task, and defining an objective function for intra-domain classifier learning; then solving the objective function to obtain a probability distribution matrix of the target domain; selecting, from the probability distribution matrix, a label corresponding to the maximum probability value corresponding to each data sample in the target domain as a prediction label of the data sample of the target domain to complete bearing fault diagnosis. The diagnosis method improves the bearing fault diagnosis accuracy and diagnosis efficiency.
BEARING FAULT DIAGNOSIS METHOD BASED ON ADAPTIVE MANIFOLD PROBABILITY DISTRIBUTION