|本期目录/Table of Contents|

[1]李琴,孙春梅,黄志强,等.兰成渝腐蚀管道失效压力的GA-BP 神经网络组合预测方法[J].中国安全生产科学技术,2015,11(11):83-89.[doi:10.11731/j.issn.1673-193x.2015.11.014]
　LI Qin,SUN Chun-mei,HUANG Zhi-qiang,et al.Combined forecasting method of GA-BP neural network for failure pressure of Lan-Cheng-Yu corroded pipelines[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2015,11(11):83-89.[doi:10.11731/j.issn.1673-193x.2015.11.014]
点击复制

兰成渝腐蚀管道失效压力的GA-BP 神经网络组合预测方法
分享到：

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
11
期数:
2015年11期
页码:
83-89
栏目:
职业安全卫生管理与技术
出版日期:
2015-11-30

文章信息/Info

Title:
Combined forecasting method of GA-BP neural network for failure pressure of Lan-Cheng-Yu corroded pipelines
文章编号:
1673-193X(2015)-11-0083-07
作者:
李琴; 孙春梅; 黄志强; 肖祥; 汤海平
西南石油大学，四川 成都 610500
Author(s):
LI Qin;  SUN Chun-mei;  HUANG Zhi-qiang;  XIAO Xiang;  TANG Hai-ping
Southwest Petroleum University, Chengdu Sichuan 610500, China
关键词:
失效压力; 遗传算法; BP神经网络; 组合预测
Keywords:
failure pressure;  genetic algorithm;  BP neural network;  combined forecasting
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2015.11.014
文献标志码:
A
摘要:
为准确掌握管道失效压力，保证管道安全运行，根据神经网络的非线性和良好的函数逼近特性，提出了基于遗传算法（GA）优化的BP神 经网络组合模型的腐蚀长输管道失效压力预测模型。组合模型将最佳组合阀值与权值隐含在网络的连接中，兼具遗传算法、人工神经网络预测 的优点，并克服了原始数据少对预测精度的影响，同时避免了神经网络容易陷入局部寻优的缺陷，也增强了网络的适应性，改善网络的收敛性 ，在客观地反应腐蚀油气管道失效压力变化趋势方面具有一定的优势。通过实例分析，结果表明：BP神经网络的预测值和Modified B31G计算结 果与真实值误差均较大，而GA-BP的预测值与实际结果的相对误差最大为6.12%，有很好的一致性，为管道的预防性维修提供了理论依据。
Abstract:
In order to grasp the failure pressure of pipeline accurately, and ensure the safe operation of pipeline, according to the nonlinear and good function approximating properties of neural network, a prediction model on failure pressure of long distance corroded pipeline was proposed by adopting the combined model of BP neural network based on genetic algorithm (GA) optimization. The combined model implies the best combination threshold and weight in the connection of network. It has the advantages of both genetic algorithm and artificial neural network forecasting, and overcomes the influence of less original data on the accuracy of prediction. At the same time, it can avoid the defect of neural network which is easy to fall into local optimization, and also enhance the adaptability of network and improve the convergence of network. It has certain superiority to reflect the change trend of failure pressure in corroded pipelines objectively. Through case analysis, the results showed that compared with the real value, the errors of forecasting value by BP neural network and calculation result of Modified B31G were both larger, while the largest relative error of forecasting value by GA-BP was 6.12%, which has a good agreement. It provides a theoretical basis for the preventive maintenance of pipeline.

参考文献/References:

［1］吴起，刘彦伟. 基于ＢＰ神经网络的安全生产事故统计情况预测分析［J］. 中国安全科学学报，2009，19(9):47-52 WU Qi,LIU Yan-wei.Statistical predicition and analysis of work safety accidents based on BP neural network［J］.Chaina Safety Science Jorunal，2009，19(9):47-52
［2］赵广利，赵新伟. 基于不同评价准则的含腐蚀缺陷管道概率评价方法研究［J］. 中国安全科学学报，2012，22(9):57-62 ZHAO Guang-li,ZHAO Xin-wei.Research on probabilistic assessment method of corroded pipeline based on different ceiteria ［J］.Chaina Safety Science Jorunal. 2012.22(9):57-62
［3］马修元，段钰锋. 基于PSO-BP 神经网络的水焦浆管道压降预测［J］. 中国电机工程学报，2012，32(5):54-60 MA Xiu-yuan, DUAN Yu-feng. Prediction of pressure drop of coke water slurry flowing in pipeline by PSO-BP neural network［J］ . Proceedings of the CSEE.2012，32(5):54-60
［4］A.A.Chojaczyk,A.P.Teixeira. Review and application of Artificial Neural Networks models in reliability analysis of steel structures［J］.Structural Safety,2015，52：78-89
［5］Mohammed S.El-Abbasy,Ahmed Senouci.Artificial neural network models for predicting condition of offshore oil and gas pipelines［J］.Automation in construction.2014.45:50-65
［6］R.C.C. Silva, J.N.C. Guerreiro. A study of pipe interacting corrosion defects using the FEM and neural networks［J］. Advances in Engineering Software，2007，38(11):868-875
［7］A.Hosni Elhewy,E.Mesbahi.Reliability analysis of structure using neural network method［J］.Probabilistic Engineering Mechanics，2006，21:44-53
［8］Jin Cheng,Q.S.Li.Reliability analysis of structure using artificial neural network based genetic algorithms ［J］.Comput.Methods Appl.Mech.Engrg,2008，197:3742-3750
［9］张德峰.MATLAB神经网络应用设计［M］.北京：机械工业出版社，2009：92-119,24-48
［10］S.K. Lahiri, K.C. Ghanta Development of an artificial neural network correlation for prediction of hold-up of slurry transport in pipelines［J］. Chemical Engineering Science，2008,63(6): 1497-1509.
［11］周忠科，王立杰.基于BP神经网络的煤矿安全预警评估机制研究［J］.中国安全生产科学技术，2011，7（4）：134-138 ZHOU Zhong-ke,WANG Li-jie.Study on safety early-warning assessment in coal mine based on bp neural networks［J］.Journal of Safety Science and Technology,2011，7（4）：134-138

相似文献/References:

[1]吴云,王鹏,胡小娟,等.基于遗传-BP算法的煤矿安全监控系统测试研究[J].中国安全生产科学技术,2011,7(6):72.
　WU Yun,WANG Peng,HU Xiao-juan,et al.Test Case Study of Coal Mine Safety Monitoring System Based on Genetic-BP Algorithm[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2011,7(11):72.
[2]袁朋伟,宋守信,董晓庆.基于灰色神经网络优化组合模型的火灾预测研究[J].中国安全生产科学技术,2014,10(3):119.[doi:10.11731/j.issn.1673-193x.2014.03.020]
　YUAN Peng wei,SONG Shou xin,DONG Xiao qing.Study on fire accident prediction based on optimized grey neural network combination model[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(11):119.[doi:10.11731/j.issn.1673-193x.2014.03.020]
[3]张钦礼,陈秋松,王新民,等.全尾砂絮凝沉降参数GA-SVM优化预测模型研究[J].中国安全生产科学技术,2014,10(5):24.[doi:10.11731/j.issn.1673-193x.2014.05.004]
　ZHANG Qinli,CHEN Qiusong,WANG Xinming,et al.Study on GA_SVM optimal prediction model on flocculating sedimentation parameter of unclassified tailings[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(11):24.[doi:10.11731/j.issn.1673-193x.2014.05.004]
[4]刘剑,宋莹,邓立军.基于GA与最长路径并联通路法优化通风网络图绘制[J].中国安全生产科学技术,2014,10(11):77.[doi:10.11731/j.issn.1673-193x.2014.11.013]
　LIU Jian,SONG Ying,DENG Li-jun.Optimization on drawing of ventilation network graph based on GA and longest parallel path method[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(11):77.[doi:10.11731/j.issn.1673-193x.2014.11.013]
[5]马欣,付晓君,余景刚,等.RBI技术应用于LNG设备上的改进研究[J].中国安全生产科学技术,2015,11(4):107.[doi:10.11731/j.issn.1673-193X.2015.04.017]
　MA Xin,FU Xiao-jun,YU Jing-gang,et al.Study on improvement for application of RBI technology in LNG equipment[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2015,11(11):107.[doi:10.11731/j.issn.1673-193X.2015.04.017]
[6]王海清,王玉秀,宋贤生,等.采用遗传算法优化气体探测器布局策略研究[J].中国安全生产科学技术,2016,12(9):86.[doi:10.11731/j.issn.1673-193x.2016.09.016]
　WANG Haiqing,WANG Yuxiu,SONG Xiansheng,et al.Study on layout optimization strategy of gas detectors using genetic algorithm[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2016,12(11):86.[doi:10.11731/j.issn.1673-193x.2016.09.016]
[7]何宁,徐波,孙恩吉.基于改进遗传-单纯形混合算法的危险气体泄漏溯源分析[J].中国安全生产科学技术,2017,13(11):54.[doi:10.11731/j.issn.1673-193x.2017.11.009]
　HE Ning,XU Bo,SUN Enji.Analysis on leakage tracing of dangerous gas based on hybrid algorithm of improved genetic algorithm and simplex method[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2017,13(11):54.[doi:10.11731/j.issn.1673-193x.2017.11.009]
[8]冉连月,吴贤国,刘洋,等.武汉市轨道交通线网应急救援站选址研究[J].中国安全生产科学技术,2018,14(4):63.[doi:10.11731/j.issn.1673-193x.2018.04.010]
　RAN Lianyue,WU Xianguo,LIU Yang,et al.Study on site selection of emergency rescue stations in Wuhan rail transit network[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2018,14(11):63.[doi:10.11731/j.issn.1673-193x.2018.04.010]
[9]何帔雨,李鹏,谢汝生,等.基于遗传算法的输电线路覆冰灾害应急响应物资储备决策优化模型[J].中国安全生产科学技术,2019,15(1):51.[doi:10.11731/j.issn.1673-193x.2019.01.008]
　HE Peiyu,LI Peng,XIE Rusheng,et al.Optimization model on decisionmaking of emergency response material reserve for icing disaster of power transmission line based on genetic algorithm[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2019,15(11):51.[doi:10.11731/j.issn.1673-193x.2019.01.008]
[10]周启超,刘剑,刘丽,等.基于SVM的通风系统故障诊断惩罚系数与核函数系数优化研究[J].中国安全生产科学技术,2019,15(4):45.[doi:10.11731/j.issn.1673-193x.2019.04.007]
　ZHOU Qichao,LIU Jian,LIU Li,et al.Research on fault fiagnosis penalty coefficient and kernel function coefficient optimization of ventilation system based on SVM[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2019,15(11):45.[doi:10.11731/j.issn.1673-193x.2019.04.007]

备注/Memo

备注/Memo:
中石油科技创新基金项目（2011D-5006-0607）；高等院校研究生创新基金项目（CX2014SY17）
更新日期/Last Update: 2015-12-15