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.
Highlights
Methane sensors are important detectors of methane concentrations under coal mine shafts
When the residual error between Y and y changes larger, the measured value (y) of the sensor (T1) to be diagnosed cannot correctly determine the methane concentrations at the monitoring points, thereby T1 is operating in fault state
With a detailed analysis of the influencing factors of methane concentrations, the generalized regression neural network (GRNN) approximation is constructed for methane sensor fault diagnosis based on multi-sensor data fusion
Summary
Methane sensors are important detectors of methane concentrations under coal mine shafts. Shao et al [6] proposed a fault diagnosis method based on a global neural network to improve the stability and reliability of a proton exchange membrane fuel cell system. Their experimental result revealed that this method was superior to others in terms of fault diagnosis efficiency and generalizing ability. Wang et al developed a prediction and evaluation program that integrated a methane detector, neural network and methane diffusion model and was different from the real-time prediction and evaluation system of harmful methane diffusion Their experimental verification revealed the high reliability of this system and its high correlation with the original evaluation model [10,11,12,13]. Information fusionbased neural networks have become one of the main development directions of methane sensor fault diagnosis
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