Fault diagnosis method and 3D visualization of accelerator device based on digital twin
A fault diagnosis method for DC 500 kV accelerator is proposed based on digital twin technology,which integrates machine learning fault diagnosis and 3D visualization system.The visual system framework is built based on device communication principles,and the real-time communication and interaction between Unity and accelerator unit equipment is completed,and the data management and experimental control functions for the equipment are achieved.The visualization systems with accelerator digital twin models are integrated,and the 3D visualization of accelerator unit equipment and new model processing functions in the system is achieved.Machine learning algorithms are used to classify,predict,and verify equipment faults in accelerator units based on vibration signals generated during equipment discharge experiments in five different states:normal operation,bearing failure,air leakage,loose base,and pump body vibration.Using decision tree algorithm,random forest algorithm and k-nearest neighbor(k-NN)algorithm,the vibration signals are simulated and trained on,and the prediction accuracy is 0.96,which means the visualized fault diagnosis of accelerator unit system is achieved.
intelligent managementdigital twin3D visualizationexperiment controlmachine learningfault diagnosisclassification and prediction
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