International Association of Online Engineering | |
Fault Diagnosis for Methane Sensors using Generalized Regression Neural Network | |
Dan Huang1  Zegong Liu2  Kaifeng Huang3  | |
[1] Huainan mining group co., LTD;School of Energy and Safety, Anhui University of Science and Technology;School of Energy and Safety, Anhui University of Science and Technology, Huainan, 232001 China | |
关键词: generalized regression neural network (GRNN); methane sensor; fault diagnosis; multi-sensor information fusion; | |
DOI : | |
学科分类:社会科学、人文和艺术(综合) | |
来源: International Association of Online Engineering | |
【 摘 要 】
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.
【 授权许可】
Unknown
【 预 览 】
Files | Size | Format | View |
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RO201904031451259ZK.pdf | 1445KB | download |