Int J Performability Eng ›› 2014, Vol. 10 ›› Issue (6): 653-657.doi: 10.23940/ijpe.14.6.p653.mag

• Short Communications • Previous Articles     Next Articles

Bearing Remaining Useful Life Prediction Based on an Improved Back Propagation Neural Network


  1. 1 Mechanical Engineering College, Shijiazhuang, CHINA
    2 The State Key Lab of Mechanical Transmission, Chongqing University, Chongqing, CHINA


Bearings are the key components in most of rotating machineries. Their failures can lead to catastrophic disasters. The accuracy of remaining useful life (RUL) prediction has a great influence on the preventive maintenance activity. RUL prediction based on standard back propagation neural network (BPNN) already exists. However, training standard BPNN needs more time and sometimes it may converge to local optima which can have contrary influence on the accuracy. Existing BPNN improving works used dynamic learning rate, momentum item and utilized genetic algorithms or other random researching algorithm to optimize the adjustment of connect weights in the network. In this paper, an improved BPNN based on Levenberg-Marquardt algorithm and momentum item is proposed. It can predict the bearing’s RUL with a good performance. Finally, the bearing simulation life data sets are used to validate the proposed method. The results show that the prediction accuracy of the proposed method is superior to other existing BPNNs.

Received on March 29, 2014; revised on June 7, 2014
References: 10