Remaining Useful Life Prediction Based on Incremental Learning
Zijun Que, Xiaohang Jin, Zhengguo Xu, Chang Hu
Abstract
Remaining useful life (RUL) prediction based on machine learning assumes that there are enough representative data for training models. However, it is impossible to have so many representative data considering security, economy factors, and so on. Thus, an incremental learning based RUL prediction approach is proposed to address this problem. First, a novel sequence input vector is constructed from the limited condition monitoring data, and it is proved that the input subspace have orthogonal properties, which is a necessary assumption to ensure the existence of a projector. Second, a projector is constructed to find a weight configuration for avoiding catastrophic forgetting. Finally, an integrated gate recurrent unit model is constructed to map the relationship between condition monitoring data and RUL. A benchmark-bearing case study, whose results indicate that the approach can update fundamental model with the acquisition of new degradation cases, demonstrates the effectiveness.