Fault diagnosis method of secondary explosion-detector sensor based on netural networks

PENG Ji-shen1; ZHOU Cheng-yong1; ZHAO Na2

doi:10.13873/j.1000-97872009.10.037

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Fault diagnosis method of secondary explosion-detector sensor based on netural networks

更新时间：2025-09-11

- Fault diagnosis method of secondary explosion-detector sensor based on netural networks
- Vol. 28, Issue 10, Pages: 26-28+31(2009)
- 作者机构：
  
  1. 辽宁工程技术大学电气与控制工程学院
  2. 辽东学院机电学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872009.10.037
  CLC： TP277
- Published：2009
- 稿件说明：
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[1]彭继慎,周程勇,赵娜.基于神经网络的二次测爆仪传感器故障诊断方法[J].传感器与微系统,2009,28(10):26-28+31.

PENG Ji-shen1, ZHOU Cheng-yong1, ZHAO Na2. Fault diagnosis method of secondary explosion-detector sensor based on netural networks[J]. 2009, 28(10): 26-28+31.
[1]彭继慎,周程勇,赵娜.基于神经网络的二次测爆仪传感器故障诊断方法[J].传感器与微系统,2009,28(10):26-28+31. DOI： 10.13873/j.1000-97872009.10.037.

PENG Ji-shen1, ZHOU Cheng-yong1, ZHAO Na2. Fault diagnosis method of secondary explosion-detector sensor based on netural networks[J]. 2009, 28(10): 26-28+31. DOI： 10.13873/j.1000-97872009.10.037.

摘要

在全面分析智能二次测爆仪中传感器主要故障的基础上

提出了基于L-M算法的神经网络诊断方案。通过模拟矿井中各种气体体积分数所占比例的仿真实验证明:基于L-M算法的神经网络故障诊断系统具有良好的故障监测与诊断能力。

Abstract

Based on the complete analysis of the major faults of the intelligent secondary explosion-detector sensors

neural network diagnosis scheme based on the Levenberg-Marquardt（L-M） algorithm is proposed.The simulation experiments of the proportion of various gas volume fraction in the mine show that the neural network fault diagnosis system has good fault monitoring and diagnostic capabilities.

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references

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气相法WO 3 纳米粉体的H 2 S气敏性能研究 [J]. 林金阳,黄世震,林伟,陈伟,黄建敏. 传感技术学报 . 2006(03)

基于改进BP神经网络的空调系统传感器故障诊断 [J]. 李冬辉,徐津津. 低压电器 . 2005(12)

矿井火区可燃性混合气体爆炸三角形判断法及其爆炸危险性分析 [J]. 周利华. 中国安全科学学报 . 2001(02)

Static and dynamic behaviour of a rigid rotor on journal bearings [J] . L. Della Pietra,E. Rosa,C. Rossi. Meccanica . 1992 (4)

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