Remaining Useful Life Prediction of Rolling Element Bearings Based on Hybrid Drive of Data and Model
Xin Wang, Lingli Cui, Huaqing Wang
Abstract
For remaining useful life (RUL) prediction of machinery, model-driven methods often use a single model to process individual data, which is difficult to adapt to the diversity of degradation behaviors. Data-driven methods are more dependent on training data, and in practice a large amount of run-to-failure data is difficult to obtain. In this paper, a new hybrid drive of data and model method is proposed. In the model-driven path, a new scalable two-stage linear/nonlinear composite model is constructed to represent various degradation behaviors, and to clarify the evolution law of individual degradation. In the data-driven path, the long short-term memory prediction network is trained to track the degradation process and learn knowledge of multi-sample degradation behavior. The newly established dynamic matching index integrates the model-driven and data-driven paths, and realizes the interactive fusion of information and RUL prediction through real-time matching of hidden layer states. The whole life cycle performance degradation data of two sets of different experimental rigs are used for analysis, and compared with some state-of-art RUL prediction methods, the results show that the proposed method has higher prediction accuracy.