基于数字孪生的加速器机组三维可视化与故障诊断方法

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中图分类号：TP302.7；TP391.99 文献标志码：B

Fault diagnosis method and 3D visualization of accelerator device based on digital twin

YANG Sheng'，LIANG Lizhen2，LIU Shaoqing2， ZHANG Xiaodan （1.School of Computer Technology and Applications，Qinghai University，Xining 81OO16，China； 2.Institute of Energy，Hefei Comprehensive National Science Center，Hefei 81OO16，China； 3.Intelligent Computing and Application Laboratory of Qinghai Province，Xining 81Oo16，China）

Abstract：A fault diagnosis method for DC 5Oo kV accelerator isproposed 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 diferent 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.

Key words： intelligent management；digital twin；3D visualization；experiment control；machine learning； fault diagnosis；classification and prediction

0 引言

在数字化、智能化发展极为迅速的新时代背景下，为实现工业设备对于数字化以及智能化的需求，基于数字孪生技术搭建信息化物理系统，并依据机器学习算法对工业数据进行数据分析及故障诊断，逐渐成为学术领域、工业领域内研究的热点。（剩余10179字）