Study on Valve Fault Diagnosis of Compression Ignition Piston Engine Based on Continuous Wavelet Transform and Model Agnostic Meta Learning
Aiming at the problems of few samples of vibration signals on the cylinder head surface of compression-ignition piston engine and the difficulty of feature extraction and selection in traditional fault diagnosis methods,a fault diagnosis method for valve lash abnormality in compression-ignition piston engine based on continuous wavelet transform(CWT)and model agnostic meta learning(MAML)was proposed.By combining the feature extraction capability of CWT and the fast learning capability of MAML,a fault diagnosis model was constructed.The experimental results show that the method can effectively identify valve lash faults,and its accuracy is higher than that of the traditional fault diagnosis method based on CWT and convolutional neural network(CNN).The effects of different valve fault types on the diagnostic capability of the model were studied through the cross-domain fault comparison experiments,and the method's performance in solving small samples and cross-domain faults was verified.The method has higher accuracy and generalization ability in solving small samples and cross-domain fault problems.
万方数据
维普
