Signal identification of fracture in gas bearing coal based on dual strategy coupling optimization

doi:10.16265/j.cnki.issn1003-3033.2022.10.1868

Abstract

Abstract:

In order to address identification problems of signal characteristics of fracture in gas bearing coal during fracture process, with Bi-directional Long Short-Term Memory network (BiLSTM) as base classifier, a dual strategy coupling optimization identification model for such signal characteristics was proposed based on AdaBoost algorithm and HHO algorithm. Firstly, in view of the problem that proportion of error samples in AdaBoost algorithm increased with iteration, which had an impact on results of final strong classifiers, weight parameters were introduced and weights of weak classifiers were changed so as to improve identification. Then, to determine optimal model parameters, identification parameters and weight parameters were optimized based on HHO, and during optimization process, HHO and improved AdaBoost algorithm produced a coupling effect, which made the identification model's accuracy and stability reach optimal level, resulting in an average accuracy of 91.36% and standard deviation being reduced to 0.017 4. The results show that the dual strategy coupling optimization model of HHO-AdaBoost-BiLSTM identification of signal characteristics of fracture in gas bearing coal has higher accuracy and stability.

Key words: fracture in gas bearing coal, identification of signal characteristics, dual strategy, coupling optimization, AdaBoost algorithm, Harris hawk optimization (HHO) algorithm

FU Hua, ZHAO Juncheng, LIU Hao, LIU Yuzhu, LU Wanjie. Signal identification of fracture in gas bearing coal based on dual strategy coupling optimization[J]. China Safety Science Journal, 2022, 32(10): 40-47.

Figures/Tables 10

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样本	P₁/ m	P₂/ MPa	P₃	P₄	P₅/ 次	P₆/ mV	P₇/ (kg·m^-1)	P₈/ MPa	P₉/ (m³·t^-1)	P₁₀/ (L·min^-1)	P₁₁/ (L·min^-1)	P₁₂/ (m³·min^-1)	P₁₃	风险等级
1	425	12.9	0.58	3	885	224	8.88	2.80	10.24	8	2.1	6.3	1	高
2	744	15.2	0.37	3	735	167	8.38	1.24	9.01	8	2.9	5.7	2	高
样本	P₁/ m	P₂/ MPa	P₃	P₄	P₅/ 次	P₆/ mV	P₇/ (kg·m^-1)	P₈/ MPa	P₉/ (m³·t^-1)	P₁₀/ (L·min^-1)	P₁₁/ (L·min^-1)	P₁₂/ (m³·min^-1)	P₁₃	风险等级
3	512	5.7	0.54	1	120	12	8.30	0.44	4.61	7	2.3	2.2	1	低
4	484	16.6	0.52	5	673	138	5.06	1.28	8.26	6	3.1	3.9	2	中
5	397	11.1	0.61	1	332	142	4.24	1.19	9.05	5	3.5	2.9	1	中
6	462	12.4	0.16	3	357	129	3.42	1.20	10.27	18	3.3	6.9	1	中
7	750	7.9	0.35	1	146	142	5.19	0.74	5.21	8	3.6	1.0	1	低
8	400	3.9	0.62	1	223	33	5.91	0.62	2.01	6	4.2	1.8	3	低
9	446	13.2	0.23	3	649	241	8.51	0.94	13.06	6	3.4	7.9	1	中
10	621	7.7	0.31	5	579	163	5.02	2.76	10.03	20	2.6	2.6	3	中

指标	填补前		填补后
指标	均值	标准差	均值	标准差
P₃	0.44	0.15	0.42	0.17
P₅	5.85	2.48	5.90	2.43
P₁₀	2.67	0.50	2.63	0.57

模型	平均准确率/%	标准差
SVM	66.64	0.065 3
ELM	71.32	0.056 9
LSTM	74.48	0.050 6
BiLSTM	77.92	0.045 2

辨识模型	平均准确率/%	标准差
PSO-BiLSTM	81.68	0.042 7
GWO-BiLSTM	85.84	0.040 7
HHO-BiLSTM	87.76	0.037 8
原始AdaBoost-BiLSTM	86.08	0.027 7
原始AdaBoost-HHO-BiLSTM	85.92	0.026 2
HHO-AdaBoost-BiLSTM	91.68	0.018 7