On-line prediction remaining useful life for ball bearings via grey NARX

Feng Liu, Qiming Niu, Qingbin Tong, Junci Cao, Yihuang Zhang
2019 Journal of Vibroengineering  
The Huge vibration data are generated continuously by many sensors in daily high-speed rotating machinery operations. Accurate online prediction based on big vibration data streaming can reduce the risks related to failures and avoid service disruptions. This paper presents a hybrid nonlinear autoregressive network with exogenous inputs (NARX) model to forecast the remaining useful life of ball bearings through health index based on big vibration data streaming. This approach is validated by a
more » ... is validated by a real data from PRONOSTIA experimentation platform and industrial test rig compared with backpropagation neural network (BP), Elman and general regression neural network (GRNN) prediction model. Root mean square error, mean absolute error and correlation coefficient were used as performance indexes to evaluate the prediction accuracy of these models. The mean absolute error, the root mean square error and the correlation coefficient of hybrid NARX model evaluation index are 2.04, 2.85 and 0.98 respectively. It shows that the model presented in this paper has higher prediction accuracy. It can meet the needs of actual ball bearing remaining useful life prediction and also provide reference in other fields.
doi:10.21595/jve.2018.20120 fatcat:lqh7rr2h6nfkbbfv5vseadm6l4