基于子领域自适应的液压系统故障诊断方法OA北大核心CSTPCD

Addressing the challenge of difficult data collection and limited datasets for hydraulic system faults,leading to lower accuracy in fault diagnosis models,a fault diagnosis method based on subdomain adaptation is proposed.Using a hydraulic system simulation model,various fault types to generate rich simulation data.Combining this simulated data with limited real-world fault data for model construction,we integrate a 1D-CNN with subdomain adaptation.This approach,employing a 1 D-CNN network and a multi-sensor information fusion strategy,comprehensively extracts fault information from different domains of hydraulic systems.In the domain adaptation layer,LMMD is utilized to reduce global and subdomain distribution differences between source and target domains.The research results demonstrate that our proposed method achieves a 100％diagnostic accuracy for each fault type with only one real-world sample per fault type,outperforming compared other intelligent fault diagnosis methods.