Fault diagnosis of gas sensor based on PCA and artificial neural network ensemble

YANG Zhen; DENG Fang-ming; HAO Yong; XU Chang-ying; WANG Hong; WU Xiang

doi:10.13873/J.1000-9787(2016)09-0033-03

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Fault diagnosis of gas sensor based on PCA and artificial neural network ensemble

更新时间：2025-09-11

- Fault diagnosis of gas sensor based on PCA and artificial neural network ensemble
- Vol. 35, Issue 9, Pages: 33-35+38(2016)
- 作者机构：
  
  1. 华东交通大学现代教育技术中心
  2. 华东交通大学电气与电子工程学院
  3. 南昌航空大学工程训练中心
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2016)09-0033-03
  CLC： TP183;TP212
- Published：2016
- 稿件说明：
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[1]杨真,邓芳明,郝勇,徐长英,王宏,吴翔.基于主元分析和集成神经网络的瓦斯传感器故障诊断[J].传感器与微系统,2016,35(09):33-35+38.

YANG Zhen, DENG Fang-ming, HAO Yong, et al. Fault diagnosis of gas sensor based on PCA and artificial neural network ensemble[J]. 2016, 35(9): 33-35+38.
[1]杨真,邓芳明,郝勇,徐长英,王宏,吴翔.基于主元分析和集成神经网络的瓦斯传感器故障诊断[J].传感器与微系统,2016,35(09):33-35+38. DOI： 10.13873/J.1000-9787(2016)09-0033-03.

YANG Zhen, DENG Fang-ming, HAO Yong, et al. Fault diagnosis of gas sensor based on PCA and artificial neural network ensemble[J]. 2016, 35(9): 33-35+38. DOI： 10.13873/J.1000-9787(2016)09-0033-03.

摘要

针对瓦斯传感器常见突发型故障

提出一种基于主元分析（PCA）和权重提升（WB）算法训练人工神经网络集成的瓦斯传感器故障诊断方法。利用PCA方法提取故障特征

得到的特征向量作为神经网络的训练样本;利用WB算法依次训练多个神经网络分类器;由集成神经网络得到待测样本的故障诊断结果。仿真实验表明:该方法对测试样本的识别正确率在98.5%以上

能够显著提高瓦斯传感器故障诊断的诊断精度和泛化能力。

Abstract

Aiming at general abrupt faults of gas sensor

a method of gas sensor fault diagnosis is proposed based on principal component analysis（PCA） algorithm and artificial neural networks integrated via weight boosting（WB） algorithm.PCA method is used to exact fault feature

feature vectors are used as training samples

several ANN classifiers are trained in turn based on WB algorithm with these samples.Fault diagnosis result of sample under test is obtained by the artificial neural networks ensemble（ANNE） classifiers.Simulation results show that

identification correct rate of this method is more than 98.5 %

it can significantly improve fault diagnosis precision and generalization performance of gas sensors.

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references

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