• CN:11-2187/TH
  • ISSN:0577-6686

›› 2002, Vol. 38 ›› Issue (3): 46-49.

• 论文 • 上一篇    下一篇

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基于动态神经网络的液压伺服系统故障检测

张若青;裘丽华   

  1. 北京航空航天大学自动化科学与电气工程学院
  • 发布日期:2002-03-15

FAULT DETECTION OF HYDRAULIC SERVO-SYSTEM BASED ON DYNAMIC NEURAL NETWORK

Zhang Ruoqing;Qiu Lihua   

  1. Beijing University of Aeronautics and Astronautics
  • Published:2002-03-15

摘要: 根据故障诊断系统的特点,采用输出递归神经网络对某液压位置伺服系统进行了故障检测研究。该动态神经网络模型的采用使网络成为系统的完全模型,避免了故障学习,可以较好地检测出较难检测的故障。通过仿真与前馈时延网络与对角递归网络的比较研究,说明了在实时故障诊断系统中输出递归网络结构的优越性。

关键词: 动态神经网络, 故障检测, 输出递归网络, 液压伺服系统

Abstract: According to the features of the fault diagnosis system, a dynamic neural model-output recurrent network, which fits for real-time fault detection is applied to the fault detection of a displacement servo-system. By applying output recurrent network, the neural model becomes the full model of the system. The simulation indicates that fault learning is avoided, and the faults which being very difficult to be detected by other methods can be detected. Comparing with time delayed feedforward network and diagonal recurrent network, output recurrent network shows its advantages in real-time fault detection system.

Key words: Dynamic neural network, Fault detection, Hydraulic servo-system, Output recurrent network

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