Rolling bearing residual life prediction method based on improved multi-granularity cascade forest

The invention discloses a rolling bearing residual life prediction method based on an improved multi-granularity cascade forest, belongs to the field of rolling bearing residual life prediction, and solves the problems of poor precision and low operation efficiency of an existing artificial intelligence method in rolling bearing residual life prediction. The method includes: firstly, carrying outiterative computation on a rolling bearing frequency domain signal obtained through fast Fourier transform to obtain iterative features; replacing a multi-granularity scanning structure in the multi-granularity cascade forest with a convolutional neural network, extracting deep features of iterative features by using the convolutional neural network, and constructing a performance degradation feature set; and then integrating a single CATBoost model capable of achieving GPU parallel acceleration, introducing a determination coefficient R2 to construct a CasCatBoost structure so as to improve the representation capabil