1. Collect open datasets of bearing imbalance, which can be obtained from major data platforms or related papers.

2. Determine the labels in the unbalance dataset, usually "unbalance" or "failure".

3. Combine the unbalanced dataset with the balanced dataset to form a new dataset. The balanced dataset can be obtained from publicly available datasets or existing datasets

4. Determine the training, validation, and testing sets of the dataset, and divide them according to a certain ratio.

5. Preprocess the dataset, including data cleaning, data normalization, feature extraction and other processes.

6, use machine learning algorithms or deep learning algorithms to train the dataset and get the model.

7. Evaluate and optimize the model to make its prediction accuracy and generalization ability better.