A Generalized Remaining Useful Life Prediction Method Based on Hybrid Model and Sparse Variational Bayesian
Wenyi Lin, Yi Chai, Xiaolong Chen, Qie Liu, Fei Feng
Abstract
The remaining useful life (RUL) prediction is one of the most important tasks in the prognostics and health management of industrial equipment. The statistical model-based method is widely used for RUL prediction, but it depends on sufficient prior knowledge and appropriate degradation assumptions, which limits its use in the case of the complexity degradation process or insufficient prior knowledge. Motivated by this, we propose a hybrid model to describe the degradation process, which is composed of some given degradation models. This can be conducive to describing complex degradation trajectories and further improving the flexibility of the degradation model. Then, a sparsity mechanism is proposed to automatically fuse these candidate degradation models based on the sparse variational Bayesian method. As a result, we can find the most appropriate degradation mechanism for a given degradation process without sufficient prior knowledge. To the best of the authors' knowledge, it is the first time to use such a fusion mechanism to describe the complex degradation process. A numerical example, a practical example, and three public datasets are used to verify the effectiveness and merits of the proposed method.