基于模态分析和神经网络的分子泵故障检测方法研究  

Research on Acoustic Detection Method for Wear Fault of Vacuum Pump

作　　者：郑悦 周盈 綦磊[1] 李一博[2] ZHENG Yue;ZHOU Ying;QI Lei;LI Yibo(Beijing Institute of Spacecraft Environment Engineering,Beijing 100094,China;University of Tianjin,Tianjin 300072,China)

机构地区：[1]北京卫星环境工程研究所,北京100094 [2]天津大学,天津300072

出　　处：《真空科学与技术学报》2024年第8期687-694,共8页Chinese Journal of Vacuum Science and Technology

基　　金：国家重点研发计划项目(2022YFB3402804)。

摘　　要：分子泵一种基于气体分子定向运动而产生真空环境的装置,在空间环境模拟等试验中至关重要,其内部结构精密而复杂,长期使用后可能会产生真空度不足等故障,如何及时准确地诊断分子泵的运行状态格外关键。文章提出了一种基于模态分析的降噪技术,并采用神经网络对采集到的信号数据进行故障诊断与识别。实验结果表明,该方法的平均诊断准确率达到了90.0%,有效地实现了分子泵的故障检测和状态评估。As an important equipment for simulating the space environment,vacuum pumps are responsible for obtaining and maintaining the high vacuum environment inside the test container.Therefore,timely and accurate diagnosis of the operating status of vacuum pumps is particularly important.In response to the above issues,this article proposes a joint denoising method combining empirical mode decomposition and wavelet packet thresholding,which uses BP neural network to conduct fault diagnosis and signal recognition research on the data of the signal acquisition system.The results show that the average accuracy of this method reaches 90.0%,which can effectively achieve fault diagnosis and state evaluation of vacuum pumps.

关 键 词：分子泵 故障诊断 小波包能量 BP 神经网络 

分 类 号：TB774[一般工业技术—真空技术]