• CN:11-2187/TH
  • ISSN:0577-6686

›› 2005, Vol. 41 ›› Issue (2): 142-147.

• 论文 • 上一篇    下一篇

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基于现代非线性理论的汽轮发电机组故障诊断技术研究

侯荣涛;闻邦椿;周飙   

  1. 南京信息工程大学计算机科学与技术系;东北大学机械工程与自动化学院;呼和浩特国能电力责任有限公司
  • 发布日期:2005-02-15

STUDY ON FAULT DIAGNOSIS TECHNIQUE TO TURBO UNIT BASED ON MODERN NONLINEAR THEORIES

Hou Rongtao;Wen Bangchun;Zhou Biao   

  1. Computer Science and technology Department, Nanjing University of Information Science and Technology School of Mechanical Engineering and Automation, Northeastern UniversityHohehot Guoneng Electric Power Co., Ltd
  • Published:2005-02-15

摘要: 运用小波理论、分形理论和混沌理论等非线性理论,对汽轮发电机组转子故障进行了综合分析和研究。对所测某28 MW发电机组转子在三种不同工作状态下的时间序列进行了关联维数计算、小波包分解以及最大李雅普诺夫指数计算,并结合其相轨迹图和庞加莱截面进行了分析与研究。结果表明,小波包分解重构技术具有很强的消噪和非平稳信号提取能力;发电机组转子在不同工作状态下其时间序列的关联维数、李雅普诺夫指数具有明显差别,且两量值相互补充、相互对应。由此提出:关联维数、最大李雅普诺夫指数可以作为刻画发电机组机械故障特征的综合量化指标。该研究为非线性运动系统的在线监测、故障诊断和状态预测开辟了有效途径。

关键词: Lyapunov指数, 非线性动力学, 分形, 故障诊断, 混沌, 汽轮发电机组, 小波分析

Abstract: Modern nonlinear theories such as wavelet theory, fractal and chaos theory are used to analyze and study malfunction of turbo unit rotor synthetically. Three time domain vibration signals of a 28 MW turbo unit at different running state are researched by using wavelet packet decomposition and reconstruction technique and by calculating their correlation dimensions and largest Lyapunov exponents, reconstructing their phase trajectories and Poincare sections attend by. The research result shows that wavelet packet decomposition and reconstruction technique is powerful in interference elimination of signal and extracting saltation signal from complex vibration system. Correlation di-mensions or largest Lyapunov exponents of the time domain vibration signal are different obviously to the generator rotor at the three unlike running states. So conclusion which correlation dimensionality and largest Lyapunov exponent can be used as synthesis quantify index to depict mechanical fault characteristic of generator set is educed. The research can be generalized to any nonlinear dynamical system and gives a new approach for monitoring condition, fault diagnosis and prediction online.

Key words: Chaos, Fault diagnosis, Fractal, Lyapunov exponent, Nonlinear dynamics, Turbo-unit, Wavelet analysis

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