Intelligent Identification Method of Industrial Mixed Gases Based on ConvGRU Fusion Attention Mechanism

doi:10.12068/j.issn.1005-3026.2025.20240164

Abstract

Abstract:

To address the issue of high data dependency and insufficient accuracy in mixed gas identification for traditional semiconductor gas sensors， a ConvGRUAttention network model that integrates gated recurrent units （GRU）， convolutional layers， and attention mechanism is proposed. Empirical wavelet transform （EWT） is employed to convert raw signals into the time-frequency domain and perform multi-scale decomposition， which suppresses noise， reduces data dependency， and enhances the model’s robustness. The model extracts local dynamic features through convolutional layers， captures long-term dependencies using GRU， and optimizes feature weights across multi-scale signals via the attention mechanism， thereby improving feature extraction and generalization capabilities. Experimental results demonstrate 100% accuracy in qualitative identification and a root mean square error （RMSE） of 3.3×10⁻⁶ in quantitative detection. Compared with the traditional methods， the detection accuracy for mixed gases is significantly improved.

Key words: semiconductor gas sensor, empirical wavelet transform （EWT）, ConvGRUAttention model, qualitative identification, quantitative detection

CLC Number:

TP 249

Fan-li MENG, Shu-chang LI, Hao WANG, Zhen-yu YUAN. Intelligent Identification Method of Industrial Mixed Gases Based on ConvGRU Fusion Attention Mechanism[J]. Journal of Northeastern University(Natural Science), 2025, 46(7): 37-48.

Figures/Tables 16

References 37

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模块名称	层次结构	卷积核	输入特征量	步长	填充	输出特征量	droput
ConvGRU	32个ConvGRU单元	3×3		3×3	1		0.1
ConvGRU	64个ConvGRU单元	3×3		3×3	1		0.1
Attention	一维平均池化层	1		1	1		0
	一维最大池化层	1		1	1		0
	线性层		64			4
	激活函数层	ReLu函数
	线性层		4			64
	激活函数层	ReLu函数
输出	一维平均池化层	16		16	1		0
	线性层		1 024			300
	线性层		300			3(类别)/2(浓度)
	激活函数层	ReLu函数

模块名称	层次结构	卷积核	输入特征量	步长	填充	输出特征量	droput
ConvGRU	32个ConvGRU单元	3×3		3×3	1		0.1
ConvGRU	64个ConvGRU单元	3×3		3×3	1		0.1
Attention	一维平均池化层	1		1	1		0
	一维最大池化层	1		1	1		0
	线性层		64			4
	激活函数层	ReLu函数
	线性层		4			64
	激活函数层	ReLu函数
输出	一维平均池化层	16		16	1		0
	线性层		1 024			300
	线性层		300			3(类别)/2(浓度)
	激活函数层	ReLu函数

模型- 气体	准确率/%
模型- 气体	IMF1	IMF2	IMF3	IMF4	IMF5	IMF6
G-T	71.88	68.25	44.12	7.12	2.12	1.12
G-X	75.12	80.88	24.38	0.00	0.00	4.25
G-TX	90.72	90.00	96.78	9.81	100.00	98.41
CG-T	94.75	96.44	38.44	15.50	39.38	48.12
CG-X	86.22	86.88	39.75	0.00	5.81	71.69
CG-TX	95.30	95.70	95.38	97.48	91.33	88.16
CGA-T	92.31	96.81	44.56	12.75	49.31	41.44
CGA-X	86.25	87.62	47.44	0.00	39.56	71.00
CGA-TX	94.77	96.83	95.36	97.89	92.86	89.52

模型- 气体	准确率/%
模型- 气体	IMF1	IMF2	IMF3	IMF4	IMF5	IMF6
G-T	71.88	68.25	44.12	7.12	2.12	1.12
G-X	75.12	80.88	24.38	0.00	0.00	4.25
G-TX	90.72	90.00	96.78	9.81	100.00	98.41
CG-T	94.75	96.44	38.44	15.50	39.38	48.12
CG-X	86.22	86.88	39.75	0.00	5.81	71.69
CG-TX	95.30	95.70	95.38	97.48	91.33	88.16
CGA-T	92.31	96.81	44.56	12.75	49.31	41.44
CGA-X	86.25	87.62	47.44	0.00	39.56	71.00
CGA-TX	94.77	96.83	95.36	97.89	92.86	89.52

模型	准确率/%			Kappa值
模型	甲苯	二甲苯	甲苯-二甲苯	Kappa值
SVM	87.50	87.50	90.62	0.796
CNN	70.88	85.38	96.59	0.799
GRU	80.00	76.12	94.03	0.768
TCN	87.50	62.50	93.75	0.750
EWTGRU	72.25	76.88	91.03	0.708
ConvGRU	96.00	85.50	97.06	0.898
Ours	100.00	100.00	100.00	1.000