基于元迁移学习的压燃式活塞发动机气门故障诊断研究OA北大核心CSTPCD

In allusion to the challenges of limited vibration signal samples and difficulties in diagnosing faults across different operating conditions in valve clearance fault diagnosis of compression-ignition piston engine,a method of compression-ignition piston engine valve clearance abnormal fault diagnosis based on meta-learning and transfer learning is proposed.In the meta-learning,MAML is used as learner to expand the support set data of the target domain,thereby enhancing its generalization ability.In the transfer learning,ResNet34 is used as the feature extraction network to replace the SL loss function with SSL to compress the distance between feature vectors of the source domain,providing more feature embedding space for the target domain task and enhancing its cross-domain diagnostic capability.The decision fusion of pre-trained and fine-tuned meta-learning and transfer learning models are used as the diagnostic result output,and experimental data validation is conducted by means of engine bench.The results show that the proposed method can effectively identify cross working condition valve clearance faults in small sample situations,and the effect is significantly better than diagnostic methods that use meta-learning or transfer learning alone.