|本期目录/Table of Contents|

[1]刘剑,刘丽,黄德,等.基于风量-风压复合特征的通风系统阻变型故障诊断[J].中国安全生产科学技术,2020,16(1):85-91.[doi:10.11731/j.issn.1673-193x.2020.01.014]
　LIU Jian,LIU Li,HUANG De,et al.Resistance variant fault diagnosis of ventilation system based on composite features of air volume and air pressure[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2020,16(1):85-91.[doi:10.11731/j.issn.1673-193x.2020.01.014]
点击复制

基于风量-风压复合特征的通风系统阻变型故障诊断
分享到：

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

卷:
16
期数:
2020年1期
页码:
85-91
栏目:
职业安全卫生管理与技术
出版日期:
2020-01-30

文章信息/Info

Title:
Resistance variant fault diagnosis of ventilation system based on composite features of air volume and air pressure
文章编号:
1673-193X(2020)-01-0085-07
作者:
刘剑; 刘丽; 黄德; 邓立军; 周启超
（1.辽宁工程技术大学 安全科学与工程学院，辽宁 葫芦岛 125105；
2.辽宁工程技术大学 矿山热动力灾害与防治教育部重点实验室，辽宁 葫芦岛 125105）
Author(s):
LIU Jian;  LIU Li;  HUANG De;  DENG Lijun;  ZHOU Qichao
(1.College of Safety Science and Engineering,Liaoning Technical University,Huludao Liaoning 125105,China;
2.Key Laboratory of Mine Thermal Dynamics and Prevention,Ministry of Education,Liaoning Technical University,Huludao Liaoning 125105,China)
关键词:
风量-风压复合特征; 矿井通风系统; 阻变型故障; 故障诊断; 蒙特卡洛;  SVM
Keywords:
composite features of air volume and air pressure;  mine ventilation system;  resistance variant fault;  fault diagnosis;  Monte Carlo;  SVM
分类号:
X936；TD72
DOI:
10.11731/j.issn.1673-193x.2020.01.014
文献标志码:
A
摘要:
为了避免风量单一特征进行故障位置诊断的不适定性，提出基于风量-风压复合特征的故障位置诊断方法，实现特征信息的多维互补，提高故障位置诊断的准确度。利用蒙特卡洛方法生成大致满足实际故障风阻值分布的故障仿真样本，为了避免不同变量之间不同量纲、不同数量级造成的数据损失，对原始风量、风压数据进行标准化处理，并分别以风量单一特征、风压单一特征、风量-风压复合特征作为支持向量机（SVM）的输入，构建通风系统阻变型故障位置诊断模型。通过故障模拟实验研究表明：风量、风压单一特征进行故障位置诊断的准确度分别为89.80%，90.34%，风量-风压复合特征进行故障位置诊断的准确度为98.23%，说明风量-风压复合特征进行故障诊断可以消除风量、风压单一特征进行故障诊断的不适定性，提高故障诊断的准确度。
Abstract:
In order to avoid the illposed characteristic of fault location diagnosis based on the single feature of air volume,a fault location diagnosis method based on the composite features of air volume and air pressure was proposed to realize the multidimensional complementarity of feature information and improve the accuracy of fault location diagnosis.The fault simulation samples which could approximately satisfy the actual distribution of fault air resistance were generated by using the Monte Carlo method.In order to avoid the data loss caused by different dimensions and orders of magnitude between different variables,the original air volume and air pressure data were standardized,and the single feature of air volume,single feature of air pressure and composite features of air volume and air pressure were used as the input of support vector machine (SVM) respectively,then a model of resistance variant fault location diagnosis for the ventilation system was built.The results of fault simulation experiments showed that the accuracy of fault location diagnosis based on single feature of air volume and air pressure was 89.80% and 90.34% respectively,while the accuracy of fault location diagnosis based on composite features of air volume and air pressure was 98.23%,the air volume and air pressure composite feature could eliminate the discomfort of air volume and air pressure single feature in fault diagnosis and improved the accuracy of fault diagnosis.

参考文献/References:

［1］刘剑,郭欣,邓立军,等.基于风量特征的矿井通风系统阻变型单故障源诊断［J］.煤炭学报,2018,43(1):143-149. LIU Jian,GUO Xin,DENG Lijun,et al.Resistance variant single fault source diagnosis of mine ventilation system based on air volume characteristic［J］.Journal of China Coal Society,2018,43( 1):143-149.
［2］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):45-51. ZHOU Qichao,LIU Jian,LIU Li,et al.Study on optimization of penalty coefficient and kernel function coefficient of ventilation system fault diagnosis based on SVM ［J］.Journal of Safety Science and Technology,2019,15 (4):45-51.
［3］龚晓燕,孙晓辉.基于遗传算法的矿井局部通风故障诊断神经网络模型［J］.煤矿安全,2008,39(5):35-38. GONG Xiaoyan,SUN Xiaohui.Neural network model for fault diagnosis of mine local ventilation based on genetic algorithm ［J］.Coal Mine Safety,2008,39(5):35-38.
［4］胡青伟.大平煤矿通风系统异常诊断研究［D］.徐州:中国矿业大学,2019.
［5］潘竟涛,赵丹,李宗翔,等.大明矿通风系统故障源诊断及风速传感器的布置［J］.煤炭学报,2013,38(S1):153-158. PAN Jingtao,ZHAO Dan,LI Zongxiang,et al.Fault source diagnosis of ventilation system and arrangement of wind speed sensor in Daming mine ［J］.Journal of China Coal Society,2013,38 (S1):153-158.
［6］赵丹.基于网络分析的矿井通风系统故障源诊断技术研究［D］.阜新:辽宁工程技术大学,2011.
［7］赵丹,陈帅,潘竞涛.基于FTA的矿井监控预警诊断知识表示及推理机制［J］.中国安全生产科学技术,2015,11(6):38-43. ZHAO Dan,CHEN Shuai,PAN Jingtao.Knowledge representation and reasoning mechanism of mine monitoring and early warning diagnosis based on FTA ［J］.Journal of Safety Science and Technology,2015,11 (6):38-43.
［8］赵丹,陈帅,潘竞涛.矿井监控预警诊断系统研究［J］.中国安全科学学报,2015,25(4):63-69. ZHAO Dan,CHEN Shuai,PAN Jingtao.Research on mine monitoring,early warning and diagnosis system ［J］.Chinese Journal of Safety Sciences,2015,25 (4):63-69.
［9］赵丹,陈占君,王东,等.RBF神经网络的矿井风速故障源［J］.辽宁工程技术大学学报(自然科学版),2013,32(6):749-753. ZHAO Dan,CHEN Zhanjun,WANG Dong,et al.RBF neural network fault source of mine wind speed ［J］.Journal of Liaoning University of Engineering and Technology (Natural Science Edition),2013,32 (6):749-753.
［10］ZHU Yongli,HUO Limin,LU Jinling.Bayesian networks-based approach for power systems fault diagnosis［J］.IEEE Transactions on Power Delivery,2006,21(2):634-639.
［11］FEI S W,ZHANG X B.Fault diagnosis of power transformer based on support vector machine with genetic algorithm［J］.Expert Systems with Applications,2009,36(8):11352-11357.
［12］李孝全,庄德慧,张强.基于粗糙径向基神经网络的电网故障诊断新模型［J］.电力系统保护与控制,2009,37(18):20-24. LI Xiaoquan,ZHUANG Dehui,ZHANG Qiang.A new fault diagnosis model for power network based on rough radial basis function neural network ［J］.Protection and Control of Power System,2009,37 (18):20-24.
［13］袁圃,毛剑琳,向凤红,等.改进的基于遗传优化BP神经网络的电网故障诊断［J］.电力系统及其自动化学报,2017,29(1):118-122. YUAN Pu,MAO Jianlin,XIANG Fenghong,et al.Improved fault diagnosis of power network based on genetic optimization BP neural network ［J］.Journal of Power System and Automation,2017,29 (1):118-122.
［14］廉毅.GIS环境下天然气管网故障诊断系统研究［D］.重庆:重庆大学,2005.
［15］陈泉博.燃气管网泄漏工况分析及故障诊断［D］.哈尔滨:哈尔滨工业大学,2008.
［16］XU Q Q,ZHANG L B,WEI L.Acoustic detection technology for gas pipe-line leakage［J］.Process Safety and Environmental Protection,2013,91(4):253-261.
［17］KAPELAN Z S,SAVIC D A,WALTERS G A.A hybrid inverse transient model for leakage detection and roughness calibration in pipe networks［J］.Journal of Hydraulic Research,2003,41(5):481-492.
［18］VI' TKOVSKY` J P,LIGGETT J A,SIMPSON A R,et al.Optimal measurement site locations for inverse transient analysis in pipe networks［J］.Journal of Water Resources Planning and Management,2003,129(6):480-492.
［19］MASHFORD J,DE S D,MARNEY D,et al.An approach to leak detection in pipe networks using analysis of monitored pressure values by support vector machine［C］//2009 Third International Conference on Network and System Security，2009.
［20］LEI C H,ZOU P H.Two-stage BP neural network leakage fault diagnosis of heating networks［J］.Journal of Harbin Institute of Technology,2011,43(2):75-79.
［21］雷翠红.供热管网泄漏故障诊断的研究［D］.哈尔滨:哈尔滨工业大学,2010.
［22］周守军,郭敏,孙浩森,等.热水集中供热管网泄漏故障诊断模型［J］.山东大学学报(工学版),2013,43(4):105-110. ZHOU Shoujun,GUO Min,SUN Haosen,et al.Leakage fault diagnosis model of hot water central heating network ［J］.Journal of Shandong University (Engineering Edition),2013,43 (4):105-110.
［23］刘剑,李雪冰,宋莹,等.基于速度场系数的主通风机风量单点统计测量方法［J］.有色金属工程,2018,8(2):114-117. LIU Jian,LI Xuebing,SONG Ying,et al.Single-point statistical measurement method for air volume of main fan based on velocity field coefficient ［J］.Nonferrous Metal Engineering,2018,8 (2):114-117.

相似文献/References:

[1]章能胜,张黔生,谢贤平,等.三种确定矿井通风系统设计方案评价指标权重的方法比较[J].中国安全生产科学技术,2012,8(12):38.
　ZHANG Neng sheng,ZHANG Qian sheng,XIE Xian ping,et al.Comparative study on three methods for the evaluation index weight of design scheme on mine ventilation system[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2012,8(1):38.
[2]韩艳杰,张志军,李亚俊.基于新型综合集成法的矿井通风系统安全评价[J].中国安全生产科学技术,2014,10(2):75.[doi:10.11731/j.issn.1673-193x.2014.02.013]
　HAN Yan jie,ZHANG Zhi jun,LI Ya jun.Evaluation of mine ventilation system safety based on new comprehensive integrated method[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(1):75.[doi:10.11731/j.issn.1673-193x.2014.02.013]
[3]程刚,陆卫东,陈志峰,等.基于FAHP-MODM的矿井通风系统可靠性综合评价[J].中国安全生产科学技术,2018,14(2):99.[doi:10.11731/j.issn.1673-193x.2018.02.016]
　CHENG Gang,LU Weidong,CHEN Zhifeng,et al.Comprehensive evaluation on reliability of ventilation system in mine based on FAHP-MODM[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2018,14(1):99.[doi:10.11731/j.issn.1673-193x.2018.02.016]
[4]马恒,苗倩斐,韩宝华,等.矿井通风系统稳定性SD预测仿真分析[J].中国安全生产科学技术,2019,15(12):108.[doi:10.11731/j.issn.1673-193x.2019.12.018]
　MA Heng,MIAO Qianfei,HAN Baohua,et al.SD prediction and simulation analysis on stability of mine ventilation system[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2019,15(1):108.[doi:10.11731/j.issn.1673-193x.2019.12.018]
[5]邵良杉,张佳琦,于保才,等.基于TF-熵权法的矿井通风系统可靠性可拓评价*[J].中国安全生产科学技术,2022,18(4):106.[doi:10.11731/j.issn.1673-193x.2022.04.015]
　SHAO Liangshan,ZHANG Jiaqi,YU Baocai,et al.Extension evaluation on reliability of mine ventilation system based on TF-entropy weight method[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(1):106.[doi:10.11731/j.issn.1673-193x.2022.04.015]

备注/Memo

备注/Memo:
收稿日期： 2019-11-06
* 基金项目： 国家自然科学基金项目(51774169)；国家重点研发计划项目(2017YFC0804401)
作者简介： 刘剑，博士，教授，主要研究方向为矿井通风与防灭火。
更新日期/Last Update: 2020-03-02