An adaptive multi-scale feature fusion and adaptive mixture-of-experts multi-task model for industrial equipment health status assessment and remaining useful life prediction

Abstract

Health status (HS) assessment and remaining useful life (RUL) prediction are two essential tasks in prognostics and health management. Though HS assessment and RUL prediction based on multi-task learning have achieved considerable progress, the following three challenges still exist. Firstly, it is assumed that these two tasks are related, and the inter-task correlation is ignored. Secondly, it is difficult to extract complex degradation features using the traditional deep network as the shared-bottom structure. Thirdly, inter-task heterogeneity is not considered, which limits the model's generalization ability to different tasks. To overcome these challenges, this work proposes an adaptive multi-scale feature fusion and adaptive mixture-of-experts multi-task model for HS assessment and RUL prediction. By exploiting inter-task affinity to measure the correlation between tasks, it provides guidance for multi-task modeling of industrial equipment. Then, an adaptive multi-scale feature fusion mechanism is designed as the shared-bottom structure to improve the feature extraction ability of the multi-task model. Besides, considering inter-task heterogeneity, an adaptive mixture-of-experts mechanism is developed to adaptively fuse expert features for specific tasks, which enhances the model's adaptability and generalization to different tasks. Finally, experiments on engine degradation data and tool wear data demonstrate that the proposed model outperforms other state-of-the-art baselines.