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

Abstract

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 capability of the model, and selecting the average life percentage p of the last cascade layer of the model to represent output; and finally, fitting p by using a linear function topredict the residual life of the bearing. The method has relatively high operation efficiency and prediction precision.

Description

technical field [0001] The invention relates to a method for predicting the remaining life of a rolling bearing, which belongs to the field of predicting the remaining life of a rolling bearing. Background technique [0002] Rolling bearings are one of the key components of rotating machinery, and their health status directly affects the operation performance of the entire equipment. Accurately predicting the life degradation trend of rolling bearings can provide valuable status information and sufficient response time for equipment maintenance. Normal operation and reduced maintenance costs of rolling bearings are of great significance [1] . [0003] Multi-grained cascade forest (multi-grained cascade forest, gcForest) is a deep learning model based on multi-grained scanning and cascade forest structure, in which the cascade forest consists of two random forests [2] . Because the multi-grain cascade forest has the advantages of less hyperparameters, model complexity can ...

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Problems solved by technology

[0007] The technical problem to be solved by the present invention is: In order to solve the problems of poor accuracy and low operation efficiency in the existing artificial intelligence method in the prediction of the remaining life of rolling bearings, the present invention further proposes a prediction of the remaining life of rolling bearings based on improved multi-grain cascade forest method

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