ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON CONVOLUTIONAL DEEP FOREST
GU Hui
SHAO Xing
WANG CuiXiang
GAO Jun
Abstract：Aiming at the vibration signal of rolling bearing with problems of nonlinear,small sample size and traditional machine learning based diagnosis algorithm required expert experience,a convolutional deep forest(CDF)based rolling bearing fault diagnosis algorithm was proposed.Firstly,the one-dimensional vibration signal was preprocessed through normalization and transformation into image.Then the convolution neural network was exploited to train the image to complete the end-to-end feature extraction,and the cascade forest was used to analyze and classify the features.Finally,the effectiveness of CDF was veri⁃fied on the bearing data set.The experimental results show that CDF can achieve high accuracy for small or big sample data under four loads.In addition,the accuracy of convolution neural network and CDF based on two-dimensional image are higher than one-dimensional,which proves the effectiveness of data preprocessing operation based on signal to image.
Keywords：Fault diagnosisRolling bearingDeep forestConvolutional neural network
Publication Date：2024-12-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：8( 1279-1286 )