摘要
为了实现准确可靠的涡轮分子泵故障诊断,提出了一种基于多样性特征和多源信息的分子泵故障诊断方法。在分子泵实验台上采集到分子泵不同故障下多个测点的振动信号,经过预处理后随机分为训练集和测试集。首先通过改变激活函数形成多个去噪自编码器,之后利用生成的深度自编码器对数据集进行多样性特征提取,最后将提取到的特征用于训练支持向量机(SVM)进行故障分类。实验结果表明该方法可以实现分子泵的准确故障诊断,准确率达到98.9%,而且在训练集不平衡或高背景噪声情况下依旧表现良好。
A novel technique of intelligent fault diagnosis of turbo-molecular pump,was developed,based on diverse features and multi-source information.The influence of the possible faults on the vibration signals was experimentally evaluated in multipoint sampling method with the lab-built test platform,theoretically analyzed and numerically processed in computation schemes.To be specific,first,the vibration signal spectra,acquired and preliminarily processed,were randomly divided into training and test sets;second,the multiple de-noising self-encoders were formed by changing the activation function;third,the generated deep auto-encoders were used to extract the features from the data set;and finally,the extracted features are used to train a support-vector-machine for fault classification.The test results show that the novel fault diagnosis technique is capable of detecting and diagnosing faults of molecular pump with an accuracy of 98.9%,even under the conditions of unbalanced training-set or high background noises.
作者
林鹏飞
陶继忠
Lin Pengfei;Tao Jizhong(Institute of Machinery Manufacturing Technology China Academy of Engineering Physics,Mianyang 621900,China)
出处
《真空科学与技术学报》
EI
CAS
CSCD
北大核心
2020年第1期33-39,共7页
Chinese Journal of Vacuum Science and Technology
基金
中国工程物理研究院超精密加工重点实验室基金资助项目(K1126).
关键词
涡轮分子泵
故障诊断
多样性特征
信息融合
去噪自编码器
Turbo-molecular pump
Fault diagnosis
Information fusion
Diverse feature extraction
Denoising autoencoder
作者简介
联系人:林鹏飞,E-mail:898896637@qq.com
