A Complementary Continual Learning Framework Using Incremental Samples for Remaining Useful Life Prediction of Machinery

Continual learning is gaining special attention in remaining useful life (RUL) prediction of machinery recently, which enables deep prognostics networks to use incremental samples to progressively improve network performance without laborious retraining. Nonetheless, current studies exhibit several constraints: 1) An explicit mechanism is lacking in preventing the loss of pivotal memories after multiple continual learning stages. 2) A sampling-enhanced replay technique is lacking for continual learning-based RUL prediction. To address the abovementioned limitations, this article proposes a complementary continual learning framework for RUL prediction of machinery, which contains two novel characteristics, i.e., long-term potentiation and associative replay. These two characteristics are complementary and coenhanced. The long-term potentiation focuses on multistage continual learning, which is able to prevent deep prognostics networks from forgetting the formerly learned degradation knowledge. The associative replay pays attention to each new continual learning stage, which is able to consolidate typical degradation knowledge into new network learning. The proposed framework is verified using run-to-failure datasets from rolling element bearings, and the framework is also compared with some state-of-the-art methods. Experimental results indicate that the proposed framework can possess lower forgetting and achieve better prognostics performance reinforcement during continual learning.