Mechatronics Equipment Performance Degradation Assessment using Limited and Unlabeled Data

Abstract

Advanced mechatronics equipment requires reliable and effective performance degradation assessment to guarantee long-term operations. Current data-driven predictions endow the operation and maintenance of mechatronic equipment flexibly and intelligently. However, the sufficient and labeled data in real industrial scenes may not be satisfied, resulting in negative impacts of overfitting and time-consuming annotations. We propose a novel prognostic model, namely unsupervised meta gated recurrent unit (UMGRU) containing a dual-cycle learning architecture with the designed clustering assignment module to deal with few-shot prognostics under unlabeled historical data. It integrates the strength of double gradient based optimizations for abstracting general degradation knowledge and offering a sensitive model status for precisely online adaptation with limited on-site data. Besides, mini-batch pseudo labels are automatically assigned within each inner cycle learning and further participate in parameter upgrades. Finally, both experimental and industrial data are used to verify the effectiveness of UMGRU.