Fault detection and identification method for hydrogen sensor based on residual of spares decomposition

WEI Bao-quan; FU Zhi-hui; DENG Fang-ming; WU Xiang; TAN Chang

doi:10.13873/J.1000-9787(2017)08-0032-03

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Fault detection and identification method for hydrogen sensor based on residual of spares decomposition

更新时间：2025-09-11

- Fault detection and identification method for hydrogen sensor based on residual of spares decomposition
- Vol. 36, Issue 8, Pages: 32-34+38(2017)
- 作者机构：
  
  华东交通大学电气与自动化工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2017)08-0032-03
  CLC： TP212
- Published：2017
- 稿件说明：
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[1]韦宝泉,付智辉,邓芳明,吴翔,谭畅.基于稀疏分解残差的氢气传感器故障探测与辨识方法[J].传感器与微系统,2017,36(08):32-34+38.

WEI Bao-quan, FU Zhi-hui, DENG Fang-ming, et al. Fault detection and identification method for hydrogen sensor based on residual of spares decomposition[J]. 2017, 36(8): 32-34+38.
[1]韦宝泉,付智辉,邓芳明,吴翔,谭畅.基于稀疏分解残差的氢气传感器故障探测与辨识方法[J].传感器与微系统,2017,36(08):32-34+38. DOI： 10.13873/J.1000-9787(2017)08-0032-03.

WEI Bao-quan, FU Zhi-hui, DENG Fang-ming, et al. Fault detection and identification method for hydrogen sensor based on residual of spares decomposition[J]. 2017, 36(8): 32-34+38. DOI： 10.13873/J.1000-9787(2017)08-0032-03.

摘要

针对传感器故障探测和诊断

提出了一种基于稀疏分解残差的氢气传感器故障探测和辨识方法。基于信号稀疏分解理论

对采集的传感器正常信号数据集

利用K奇异值分解（K-SVD）学习算法得到一超完备字典D;在字典上对非正常（故障）信号进行分解

根据稀疏分解的残差大小和范围完成对传感器故障的探测及辨识。实验结果表明:对氢气传感器的故障探测率和总辨识率分别达到98.75%和97.25%

可以有效地解决氢气传感器的故障探测和辨识。

Abstract

Aiming at detection and diagnosis of sensor

a fault detection and identification method for hydrogen sensor based on residual of spares decomposition is proposed. The method bases on theory of signal spares representation

collects the normal signal data of hydrogen sensor to learn an over-complete dictionary D by K-SVD learning algorithm firstly

then uses the dictionary D to decompose abnormal（ fault） signals and get the decomposed residuals. Finally

according to size and range of the residuals

the sensor faults can be detected and identified. The experiment results show that for hydrogen sensor

the detection and total recognition rate of the proposed method reachs to 98. 75 % and 97. 25 % respectively

which can be applied to detect and identify the fault of hydrogen sensor effectively.

关键词

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references

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多传感器集成氢气检测系统的知识推送故障诊断 [J]. 王冰,张洪泉,宋凯,张震宇. 光学精密工程 . 2015(06)

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