In order to solve the problem of poor real-time prediction of rolling bearings fault in multiple units, a width neural network method based on enhanced node update is proposed. Firstly, the width neural network is used to train the rolling bearings original vibration data after preprocessing, then the weights are updated using adding enhanced nodes in training process, finally, the width network is used to predict the data set in the sliding window and output the final results. The experimental results of the multiple units’ rolling bearings fault data show that the training time of the model can be shortened, and the prediction time can be controlled within 30 ms, which meets the requirements of the actual industrial equipment prediction; the prediction accuracy of width neural network based on enhanced node update is also guaranteed, and the prediction real-time performance is better than other methods.