A Multitask Learning Approach With Meta Auxiliary Generation Network for Remaining Useful Life Estimation

The Industrial Internet of Things (IIoT) has greatly facilitated prognostics and health management of complex mechanical equipment by enabling seamless data collection from interconnected devices, providing rich data sets crucial for advancing data-driven prognostics methodologies. In data-driven prognostics field, multitask learning (MTL) is a prominent way to acquire robust remaining useful life (RUL) estimation models by extracting complementary degradation information from highly related auxiliary tasks. However, as the key component of MTL, the auxiliary task is often designed manually with substantial effort, while not considering how to build a reasonable auxiliary task that can provide more worthy information for the primary RUL estimation task. In response to the challenge, this article proposed a MTL approach with meta auxiliary generation network for RUL estimation. First, a novel meta auxiliary generation network is developed to perform auxiliary task design. The network can generate auxiliary task labels automatically. It is updated by meta learning strategy with the RUL estimation loss as the main objective function, ensuring that the performance of the RUL estimation task can be significantly enhanced. Additionally, soft labels are introduced to the automated design to reduce the label scale difference between regression and classification tasks, and a new label regularization strategy is designed for improving the degradation consistency between regression and classification labels. Experiments on the turbofan engine and wind turbine gearbox data sets verify the effectiveness and superiority of the proposed approach.