RS-PSO-ELM下腐蚀管道失效压力预测

doi:10.16265/j.cnki.issn1003-3033.2021.03.004

中国安全科学学报 ›› 2021, Vol. 31 ›› Issue (3): 28-34.doi: 10.16265/j.cnki.issn1003-3033.2021.03.004

RS-PSO-ELM下腐蚀管道失效压力预测

骆正山教授, 田珮琦

西安建筑科技大学管理学院,陕西西安 710055

收稿日期:2020-12-10 修回日期:2021-02-06 出版日期:2021-03-28 发布日期:2021-12-20
作者简介:骆正山 (1969—),陕西汉中人,博士,教授,博士生导师,主要从事管理科学与工程、信息管理与信息系统、油气管道风险评估等方面的教学与科研工作。E-mail:luozhengshan@163.com。
基金资助:
国家自然科学基金资助(41877527);陕西省社科基金资助(2018S34)。

Prediction of failure pressure of corrosion pipelines based on RS-PSO-ELM

LUO Zhengshan, TIAN Peiqi

School of Management, Xi'an University of Architecture & Technology, Xi'an Shaanxi 710055, China

Received:2020-12-10 Revised:2021-02-06 Online:2021-03-28 Published:2021-12-20

摘要/Abstract

摘要： 为提高腐蚀管道失效压力的预测精度并简化其计算过程,提出基于粗糙集(RS)和粒子群算法(PSO)融合极限学习机(ELM)的腐蚀管道失效压力预测模型。通过属性约简提取影响失效压力的关键因素,选用PSO优化ELM的输入权值和隐含层偏差,将归一化的核心指标数据代入计算。结果表明:该模型预测结果与实际值基本一致,与单一ELM模型相比,预测结果的均方差(MSE)降至0.255;与其他蚀管道失效压力评价模型相比,该模型预测结果的绝对误差平均值降至0.32。

关键词: 粗糙集(RS), 粒子群算法(PSO), 极限学习机(ELM), 腐蚀管道, 失效压力

Abstract: In order to improve prediction accuracy of corrosion pipelines' failure pressure and simplify its calculation process, a prediction model based on RS, PSO and ELM was proposed. Key factors that affected failure pressure were extracted in a way of attribute reduction, PSO was selected to optimize input weight and hidden layer deviation of ELM, and normalized core index data were computed in calculation. The results show that prediction of the model is basically consistent with actual values, its mean square error (MSE) is reduced to 0.255 compared with single ELM model, and absolute mean error is reduced to 0.32 compared with other assessment models of failure pressure.

Key words: rough set (RS), particle swarm optimization(PSO), extreme learning machine (ELM), corrosion pipelines, failure pressure

中图分类号:

X944.4

骆正山, 田珮琦. RS-PSO-ELM下腐蚀管道失效压力预测[J]. 中国安全科学学报, 2021, 31(3): 28-34.

LUO Zhengshan, TIAN Peiqi. Prediction of failure pressure of corrosion pipelines based on RS-PSO-ELM[J]. China Safety Science Journal, 2021, 31(3): 28-34.

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RS-PSO-ELM下腐蚀管道失效压力预测

Prediction of failure pressure of corrosion pipelines based on RS-PSO-ELM

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