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Fault prediction and health management system(PHM) can effectively guarantee the reliability and safety of modern engineering system under the condition of high complexity. In mechanical fault diagnosis with high dimensional characteristic quantity of raw data collected for the more complex, and in the practical application trend prediction precision, this paper presents a method based on principal component analysis (PCA) method to predict bearing fault trend and random forests algorithm. The method uses PCA to reduce the characteristic data of the original bearing data, and selects the principal component characteristic quantity to output the nonlinear time series data. The original data are processed by PCA nonlinear time series, the sequence as a random forest algorithm input fault trend prediction, and the prediction results were compared with the BP neural network model prediction results, results show that the random forest in the fault trend prediction in precision compared with the BP neural network is improved, is a method of forecasting an effective fault trend.