Aero-engine remaining useful life prediction method with self-adaptive multimodal data fusion and cluster-ensemble transfer regression

Remaining useful life (RUL) prediction based on multimodal sensing data is indispensable for predictive main-tenance of aero-engine under cross-working conditions. Although data-driven methods have emerged as a powerful tool in RUL prediction, it is still limited in industrial applications because the majority of existing methods manually select or fuse multisensory data and ignore the inconsistency of the sensing data collected from different engines. Therefore, an intelligent RUL prediction approach is proposed for aero-engine by inte-grating multimodal data fusion methodology and ensemble transfer learning technology to dynamically select sensing data and make a robust RUL prediction under cross-working conditions. Specifically, a self-adaptive dynamic clustering approach is developed to select useful multimodal data into different clusters, each of which has a consistent degradation tendency. Furthermore, a cluster-ensemble transfer regression network is constructed by building multiple regressors for different clusters to predict the RUL values of aero-engine under cross-working conditions, where a multi-level feature learning strategy is provided to learn the domain-invariant temporal degradation knowledge. Comparative experiments are conducted on the N-CMAPSS dataset released in 2021. The results show that the proposed method outperforms other state-of-the-art RUL prediction methods.