FAULT DIAGNOSIS OF ROTATING MACHINERY USING BACK PROPAGATION NEURAL NETWORK

Liu Zhansheng; Wu Xinhua; Xia Songbo

Chinese

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FAULT DIAGNOSIS OF ROTATING MACHINERY USING BACK PROPAGATION NEURAL NETWORK

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- FAULT DIAGNOSIS OF ROTATING MACHINERY USING BACK PROPAGATION NEURAL NETWORK
- Vol. 12, Issue 3, (1997)
- 作者机构：
  
  哈尔滨工业大学
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：1997
- 稿件说明：
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Liu Zhansheng, Wu Xinhua, Xia Songbo. FAULT DIAGNOSIS OF ROTATING MACHINERY USING BACK PROPAGATION NEURAL NETWORK[J]. 1997, 12(3).
DOI：

Liu Zhansheng, Wu Xinhua, Xia Songbo. FAULT DIAGNOSIS OF ROTATING MACHINERY USING BACK PROPAGATION NEURAL NETWORK[J]. 1997, 12(3). DOI：

摘要

根据一双跨转子实验台

模拟了转子与静子在轮盘处及轴颈处碰磨、轴系不对中及转子不平衡故障

通过一个信号自动处理装置记录下转子正常振动信号及发生各种故障时的信号

然后利用研制的人工神经网络系统对故障示例进行学习。通过在实际中诊断故障

证明这种是可行的。本文还针对人工神经网络(BP算法)存在的训练速度慢的问题

提出了一个加快网络训练速度的新方法(ARBP算法)

较大提高了网络的训练速度。

Abstract

The faults of rub between rotor and stator

misalignment test rig of the shafting

and unbalance of the rotor are simulated at a compressor test rig.The normal vibration signals of the rotor and the signals of the various emerging faults are registered and sampled by an automatic signal proceessor.Then a new-developed back propagation neural network is employed to learn the fault diagnosis from the sampled faults and a new method for accelerating the learning speed has been proposed in this paper.

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Related Institution

Collaborative Innovation Center for Advanced Aero-Engine

School of Power and Energy,Northwestern Polytechnical University

沈阳工业大学机械工程学院

东北大学机械工程与自动化学院

西安工业大学机电工程学院

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