流体机械2016，Vol.44Issue(3)：11-17,7.DOI:10.3969/j.issn.1005-0329.2016.03.003
基于 DHMM的机械密封端面膜厚识别技术的研究
Mechanical Seal End Face Film Thickness State Recognition Based on DHMM
摘要
Abstract
To maintain the mechanical seal end face at a certain state of film thickness is the key to ensure the normal operation of mechanical seal.The signal acquired by Acoustic Emission(AE)method to monitor the mechanical seals usually has a low SNR signal,which makes the classification of the working condition of mechanical seals difficult.A new method of mechanical seal end face film thickness state recognition was proposed based on the Acoustic Emission signals,and the Ensemble Empirical Mode De-composition (EEMD)and Discrete Hidden Markov Model (DHMM)were introduced into the mechanical seal work condition monitoring analysis.First,the AE signals were decomposed by EEMD after they were divided into some equal frames.Then time domain characteristics and frequency domain characteristics of each frequency component were extracted.Secondly,the processing of the characteristic parameter reduction was optimized by Kernel Principal Component Analysis(KPCA).The simplified charac-teristic parameter vectors were used to train the DHMMof each film thickness state.Finally,mechanical seal end face contact state monitoring could be achieved by trained DHMM.Studies have shown that this method can identify the film thickness state of me-chanical seal end face effectively and quickly with less samples needed and fast training rate.It is important to the development of intelligent online monitoring of mechanical seal end face contact state.关键词
机械密封/状态识别/离散隐马尔科夫模型/总体经验模式分解Key words
mechanical seal/state recognition/discrete hidden markov model (DHMM)/ensemble empirical mode decomposi-tion(EEMD)分类
机械制造引用本文复制引用
张菲,傅攀,樊巍..基于 DHMM的机械密封端面膜厚识别技术的研究[J].流体机械,2016,44(3):11-17,7.基金项目
中央高校基本科研业务费专项资金资助项目 （）
