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 verified 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.