Prediction of occupational health damage caused by coal mine noise based on t-SSA-BP

doi:10.16265/j.cnki.issn1003-3033.2023.12.2068

Abstract

Abstract:

In order to accurately predict the occupational health damage of workers exposed to noise in coal mines, the influencing factors were selected based on the quantile plot method. According to the noise occupational health damage assessment method, the adaptive t distribution variation operator and SSA were used as parameter optimization algorithms to establish the t-SSA-BP coal mine noise occupational health damage prediction model, and the benchmark function was used to test the optimization performance of the algorithm. 10 coal mines in northern Shaanxi were taken as the research objects, and the prediction model was verified by field investigation, theoretical analysis and Matlab simulation. The results show that the noise exposure intensity, individual age, length of service and noise exposure post are the influencing factors of noise health damage in coal mines. The overall accuracy of t-SSA is 66.0%, which is higher than that of SSA in four benchmark functions. The order of accuracy of the five prediction models is: t-SSA-BP>SSA-BP>PSO-BP>CFA-PSO-RBF>PSO-GRNN. Compared with SSA-BP, the MAE and MAPE of t-SSA-BP prediction model decreases by 68.1% and 66.7% respectively, and the R² reaches 0.999. The prediction accuracy and convergence rate are significantly improved.

Key words: adaptive t distribution mutation operator, sparrow search algorithm (SSA), coal mine noise, prediction of health damage, BP neural network

GAO Xiaoxu, GAO Lu, PAN Xiangxu, GAO Xiang, MA Hao. Prediction of occupational health damage caused by coal mine noise based on t-SSA-BP[J]. China Safety Science Journal, 2023, 33(12): 214-222.

Figures/Tables 12

Fig.1

Fig.2

Fig.3

Fig.4

Tab.1

Benchmark function

函数	变量特征	函数表达式	取值范围	维数
F1	单峰可分离	$f 1 (x) = ∑ i = 1 30 i x i 4 + r n d$	[-1.28,1.28]	30
F2	单峰不可分离	$f 2 (x) = ∑ i = 1 30 - 1 [100 (x i + 1 - x i 2) 2 + (x i - 1) 2]$	[-30,30]	30
F3	多峰可分离	$f 3 (x) = ∑ i = 1 30 [x i 2 - 10 c o s (2 π x i) + 10]$	[-5.12,5.12]	30
F4	多峰不可分离	$f 4 (x) = 14000 ∑ i = 1 30 x i 2 - ∏ i = 1 30 c o s x i i + 1$	[-600,600]	30

Tab.1

Fig.5

Tab.2

Tab.3

Fig.6

Fig.7

Fig.8

Tab.4

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函数	算法	最优值	最劣值	平均值	标准差
F1	SSA	3.701×10^-4	3.083×10^-3	2.577×10^-3	1.676×10^-3
F1	t-SSA	2.229×10^-4	2.962×10^-3	9.817×10^-4	7.569×10^-4
F2	SSA	4.219×10^-7	2.447×10^-2	2.994×10^-3	5.507×10^-3
F2	t-SSA	2.106×10^-7	1.795×10^-2	2.774×10^-3	4.857×10^-3
F3	SSA	5.684×10^-14	7.425×10^-2	3.721×10^-3	1.618×10^-2
F3	t-SSA	5.680×10^-14	6.629×10^-4	3.334×10^-5	1.444×10^-4
F4	SSA	1.110×10^-16	1.827×10^-3	9.136×10^-5	3.982×10^-4
F4	t-SSA	1.110×10^-16	7.756×10^-5	3.907×10^-6	1.689×10^-5

样本编号	接噪岗位(标签)	检测地点	L_EX,8h/dB(A)	年龄	工龄/a	DALY/10^-6a
1	采煤机司机(1)	采煤机司机操作位	98.2	46	10	7.09
2	刮板机司机(2)	刮板输送机机头	87.1	50	15	5.63
3	破碎机司机(3)	破碎机旁	88.3	44	13	6.94
4	马蒂尔司机(4)	带式输送机自移机尾	93.1	48	11	6.34
︙	︙	︙	︙	︙	︙	︙
608	拣矸工(17)	拣矸胶带放矸口	83.7	42	10	6.93
609	选煤工(18)	选煤厂一层	79.5	43	6	6.27
610	主通风机司机(19)	主通风机操作处	79.1	44	5	6.01
611	空压机司机(20)	空压机房	81.2	29	1	8.26
612	水处理工(21)	值班室	74.6	34	3	7.23

算法	MSE/10^-15		MAE/10^-8		MAPE		R²
算法	训练集	测试集	训练集	测试集	训练集	测试集	训练集	测试集
SSA-BP	0.542	1.397	1.622	2.356	0.002	0.003	0.999	0.997
t-SSA-BP	0.087	0.116	0.663	0.751	0.001	0.001	0.999	0.999
PSO-BP	28.654	20.600	12.731	10.721	0.018	0.014	0.967	0.960
PSO-GRNN	0.306	35.945	1.053	14.234	0.001	0.018	0.999	0.930
CFA-PSO-RBF	22.112	22.479	10.524	10.130	0.014	0.013	0.974	0.956

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