Meta-learning with deep flow kernel network for few shot cross-domain remaining useful life prediction

Reliable prediction of the remaining useful life (RUL) is important for improving maintenance efficiency, equipment availability, and avoiding catastrophic accidents in complex industrial systems. Existing RUL prediction models have made some contribution, relying mainly on a large amount of degraded data with similar patterns or approximate distributions. However, in practical industrial systems, only a small amount of labeling data is usually available, which may also come from different devices and different working conditions, resulting in different distributions in the degraded data. This situation makes the existing RUL methods difficult to achieve satisfactory generalization performance. To address this challenge, this paper proposes a novel Meta-Learning with Deep Flow Kernel Network (MetaDFKN) model for RUL prediction under the few shot and cross-domain conditions. The model first learns kernel features in a data-driven manner and considers them as latent variables to improve the model's representative ability of shared knowledge between tasks. Then, we introduce the conditional normalized flow technique to infer richer posterior distributions in the kernel features, which helps to obtain feature information with stronger discriminative power. Moreover, shared knowledge and task-specific information are integrated into the contextual inference process, which can mine the dependencies of related tasks and capture richer domain information. Finally, to evaluate the proposed model, we conduct extensive experiments on engine and bearings degradation data, and the results verify the superiority of the model.