Due to the limited bearing degradation data under actual working conditions

it is impossible to obtain enough degradation data to train the neural network

it is difficult to obtain good prediction results in the deep learning network

so a new fusion method was proposed.Firstly

the features of the original vibration signal was extracted

dozens of dimensional features were obtained through the ensemble empirical mode decomposition(EEMD)and the singular value decomposition(SVD)

and the effective features such as kurtosis and mean value commonly used in remaining useful life prediction were added

then the decision tree to filter out 15⁃dimensional features was used the data was obtained by double exponential model fitting and the degraded signal was reduced to a linear trend through t⁃SNE.The linear degradation trend has better generalization in prediction than the exponential trend

and the prediction accuracy is superior to support veotor regression(SVR)and deep belief network(DBN)model.