Fault Diagnosis for Methane Sensors using Generalized Regression Neural Network

Authors

  • Kaifeng Huang School of Energy and Safety, Anhui University of Science and Technology, Huainan, 232001 China
  • Zegong Liu School of Energy and Safety, Anhui University of Science and Technology
  • Dan Huang Huainan mining group co., LTD

DOI:

https://doi.org/10.3991/ijoe.v12i03.5443

Keywords:

generalized regression neural network (GRNN), methane sensor, fault diagnosis, multi-sensor information fusion

Abstract


To identify the hang, collision and drift faults of methane sensors, this paper presents a fault diagnosis method for methane sensors using multi-sensor information fusion. A methane concentration monitoring approximation model with multi-sensor information fusion is established based on generalized regression neural network (GRNN).The output of the neural network is compared with the measured value of the sensor to be diagnosed to obtain the variation curve of the residual error signal. Through the analysis of the variation tendency of the residual error signal, the fault status of a methane sensor could be determined based on a reasonable threshold. Through simulation comparison is applied between the two models of GRNN and BP neural network; verify the GRNN model is much more precise in the approximation of methane concentrations. Fault diagnosis for methane sensors using generalized regression neural network is effective and more efficient.

Author Biographies

Kaifeng Huang, School of Energy and Safety, Anhui University of Science and Technology, Huainan, 232001 China

School of Energy and Safety, Anhui University of Science and Technology

Zegong Liu, School of Energy and Safety, Anhui University of Science and Technology

School of Energy and Safety, Anhui University of Science and Technology

Downloads

Published

2016-03-31

How to Cite

Huang, K., Liu, Z., & Huang, D. (2016). Fault Diagnosis for Methane Sensors using Generalized Regression Neural Network. International Journal of Online and Biomedical Engineering (iJOE), 12(03), pp. 42–47. https://doi.org/10.3991/ijoe.v12i03.5443

Issue

Section

Papers