引用本文:王聪,司文杰,文彬鹤,张明明,王勇,侯安平.轴流压气机旋转失速建模与检测II: 基于北航低速压气机试验台的实验研究[J].控制理论与应用,2014,31(10):1414~1422.[点击复制]
WANG Cong,SI Wen-jie,WEN Bin-he,ZHANG Ming-ming,WANG Yong,HOU An-ping.Modeling and detection of rotating stall in axial flow compressors, II: Experimental study for low-speed compressor in Beihang University[J].Control Theory and Technology,2014,31(10):1414~1422.[点击复制]
轴流压气机旋转失速建模与检测II: 基于北航低速压气机试验台的实验研究
Modeling and detection of rotating stall in axial flow compressors, II: Experimental study for low-speed compressor in Beihang University
摘要点击 3372  全文点击 1065  投稿时间：2014-02-19  修订日期：2014-07-20
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DOI编号  10.7641/CTA.2014.40115
  2014,31(10):1414-1422
中文关键词  轴流压气机  旋转失速  喘振  故障检测  确定学习  模式识别  在线实验
英文关键词  axial compressor  rotating stall  surge  fault detection  deterministic learning theory  pattern recognition  online experiment
基金项目  国家杰出青年科学基金资助项目(61225014); 国家自然青年科学基金资助项目(51306003); 国家自然科学基金重点项目(60934001).
作者单位E-mail
王聪* 华南理工大学 自动化科学与工程学院 wangcong@scut.edu.cn 
司文杰 华南理工大学 自动化科学与工程学院  
文彬鹤 中航工业航空动力控制系统研究所  
张明明 北京大学 力学与工程科学系  
王勇 北京大学 力学与工程科学系  
侯安平 北京航空航天大学 能源与动力工程学院  
中文摘要
      轴流压气机旋转失速和喘振的提前检测对于提高压气机工作效率和稳定性具有重要的意义. 本文以北京航空航天大学航空发动机重点实验室的低速轴流压气机实验台为研究对象, 基于确定学习理论及动态模式识别方法, 开展旋转失速初始扰动近似准确建模和快速检测研究. 首先, 在压气机机匣壁面周向布置多个动态压力传感器, 获取压气机失速前和失速先兆的动态压力信号, 基于确定学习理论对旋转失速初始扰动的内部系统动态进行 建模; 其次, 基于以上建模, 利用微小振动故障检测方法 实现对旋转失速的 离线和在线提前检测. 实验结果表明, 本文所提方法能够在不同 转速情况下, 提前0.3s--1s实现对旋转失速的实时在线检测.
英文摘要
      Early detection of rotating stall and surge in axial flow compressors is of great importance for improving the working efficiency and stability of the compressor. Based on deterministic learning (DL) theory and dynamical pattern recognition, this paper presents experimental research for approximately accurate modeling and rapid detection of stall precursors, and then employs a low-speed axial flow compressor test rig of Beihang University for online experimental verification. Firstly, by installing high response dynamic pressure transducers arranged circumferentially around the casing of the axial compressor, the dynamic pressure data are collected. Based on deterministic learning theory, the system dynamics underlying prestall and stall inception patterns are identified. Secondly, based on modeling results, rapid detection of small oscillation faults is used to perform the detection of stall precursors. Sufficient online experiments are conducted to investigate the efficiency of the approach. Results show that, in different working speeds, this approach successfully detects inception signal of aerodynamic instability of the compressor 0:3 s – 1 s in advance to the start of rotating stalls.