Incremental Health State Assessment of Guide Bearing in Nuclear Circulating Water Pump with Multi-signal Fusion Network
As the traditional deep-learning-based health assessment model trained on previous data is retrained on the newly coming data,catastrophic forgetting usually happens and results in an accuracy drop.To deal with this limitation,this paper proposes an incremental health assessment of guide bearing in circulating water pumps with a multi-signal fusion network.First,the multi-signal fusion network is designed to generate fusion high-level features.Then,the knowledge from both old task and new task is distilled and learned by minimizing distil-lation loss and cross-entropy loss during network training under the help of old exemplars,respectively,and the incremental health state assessment is achieved through a nearest-mean-of-exemplars classification strategy.Fi-nally,the effectiveness of the proposed method is verified by the degradation dataset collected under the circulat-ing water pump test bench.Experiment results show that the health state of bearing is assessed with an accuracy of over 94%.
nuclear circulating water pumpguide bearinghealth state assessmentincremental learning
NETL
NSTL
万方数据
