基于改进BP的分子泵故障诊断研究  

Molecular Pump Fault Diagnosis Based on Improved BP

作　　者：周阳 袁啸林 江明[1] 左桂忠 刘鹏[1] 侯吉磊 陈跃 庄会东[2] 成志新 ZHOU Yang;YUAN Xiaolin;JIANG Ming;ZUO Guizhong;LIU Peng;HOU Jilei;CHEN Yue;ZHUANG Huidong;CHENG Zhixin(Key Laboratory of Advanced Perception and Intelligence Control of High-end Equipment,Anhui Polytechnic University,Wuhu 241000,China;Institute of Plasma Physics,Chinese Academy of Science,Hefei 230031,China)

机构地区：[1]安徽工程大学高端装备先进感知与智能控制教育部重点实验室,芜湖241000 [2]中国科学院等离子体物理研究所,合肥230031

出　　处：《真空科学与技术学报》2024年第3期220-228,共9页Chinese Journal of Vacuum Science and Technology

基　　金：国家自然科学基金青年基金项目(11905254;12105322);安徽省自然科学基金青年基金项目(2108085QA38)。

摘　　要：分子泵为EAST装置提供洁净的真空环境,其运行状态影响EAST实验的顺利开展。由于在EAST实验运行过程中,分子泵设备可能会出现异物坠入或者真空泄漏故障,对装置造成次生危害。针对分子泵故障数据集不平衡导致故障诊断精度低以及模型过拟合问题,提出一种基于时域频域预处理与改进BP相结合的算法,实现分子泵故障诊断。通过在BP神经网络的基础上,引入粒子群算法(PSO)并结合五折交叉验证优化模型。首先在模拟分子泵故障的破坏性测试平台上,采集正常态、真空泄漏以及异物坠入故障振动信号,然后对数据进行时域频域特征提取融合,将得到的特征向量集作为优化算法的输入,对模型进行训练,实现分子泵故障诊断。经实验验证,所提出改进BP算法在诊断精确率上可以达到96.84%,优于支持向量机(SVM)、K近邻(KNN)和BP算法。The molecular pump provides a clean vacuum environment for the EAST device,and its running state affects the smooth development of the EAST experiment.During the operation of the EAST experiment,the molecular pump equipment may suffer from foreign matter falling in or vacuum leakage,causing secondary hazards to the device.Aiming at the problem of low fault diagnosis accuracy and model over-fitting caused by the imbalance of molecular pump fault data set,an algorithm based on the combination of time and frequency domain preprocessing and improved BP was proposed to realize molecular pump fault diagnosis.On the basis of BP neural network,particle swarm optimization(PSO)is introduced and combined with five-fold cross-verification to optimize the model.Firstly,vibration signals of normal,vacuum leakage and foreign body falling faults are collected on a destructive test platform simulating molecular pump faults,and then the data are extracted and fused in the time domain and frequency domain.The obtained feature vector set is used as the input of the optimization algorithm to train the model and realize molecular pump fault diagnosis.The experimental results show that the diagnostic accuracy of the proposed improved BP algorithm can reach 96.84%,which is superior to support vector machine(SVM),K-nearest neighbor(KNN)and BP algorithm.

关 键 词：分子泵 时域频域分析 粒子群算法 交叉验证 故障诊断 

分 类 号：TB752.27[一般工业技术—真空技术]