|本期目录/Table of Contents|

[1]潘伟,徐刚,熊方杰.基于非线性技术锅炉故障分析*[J].中国安全生产科学技术,2020,16(12):176-182.[doi:10.11731/j.issn.1673-193x.2020.12.028]
　PAN Wei,XU Gang,XIONG Fangjie.Analysis of boiler fault based on nonlinear technology[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2020,16(12):176-182.[doi:10.11731/j.issn.1673-193x.2020.12.028]
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基于非线性技术锅炉故障分析*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
16
期数:
2020年12期
页码:
176-182
栏目:
职业安全卫生管理与技术
出版日期:
2020-12-31

文章信息/Info

Title:
Analysis of boiler fault based on nonlinear technology
文章编号:
1673-193X(2020)-12-0176-07
作者:
潘伟; 徐刚; 熊方杰
（中南大学 资源与安全工程学院，湖南 长沙 410083）
Author(s):
PAN Wei;  XU Gang;  XIONG Fangjie
(School of Resources and Safety Engineering,Central South University,Changsha Hunan 410083,China)
关键词:
锅炉故障; 故障诊断; 关联维数; Lyapunov指数; 混沌特征
Keywords:
boiler fault;  fault diagnosis;  correlation dimension;  Lyapunov exponent;  chaotic characteristic
分类号:
X928.3
DOI:
10.11731/j.issn.1673-193x.2020.12.028
文献标志码:
A
摘要:
为预测和判断锅炉故障的发生，完善锅炉故障检测技术，基于锅炉运转具有非线性特点，提出将非线性评价指标应用于锅炉故障诊断。研究特征信号的非线性特性，通过确定延迟时间和嵌入维数来重构相空间；采用混沌分形理论研究非线性评价指标；提取最大Lyapunov指数和关联维数，并为收集的每种特征信号选择最合适的非线性指标。结果表明：蒸汽压力的非线性特征指标应选择关联维数，烟气温度1、烟气温度2、水管温度1和水管温度2的非线性特征指标应当选择最大Lyapunov指数。
Abstract:
In order to predict and judge the occurrence of boiler fault and improve the detection technology of boiler fault,based on the nonlinear characteristics of boiler operation,the nonlinear evaluation indexes were applied in the boiler fault diagnosis.Firstly,the nonlinear characteristics of the characteristic signals were studied,and the phase space was reconstructed by determining the delay time and embedding dimension.Secondly,the nonlinear evaluation indexes were studied by using the chaotic fractal theory.Finally,the maximum Lyapunov exponent and correlation dimension were extracted,and the most appropriate nonlinear index was selected for each characteristic signal collected.The results showed that the correlation dimension should be selected as the nonlinear characteristic index of steam pressure,and the maximum Lyapunov exponent should be selected as the nonlinear characteristic index of flue gas temperature 1,flue gas temperature 2,water pipe temperature 1 and water pipe temperature 2.

参考文献/References:

［1］王文标,田志远,汪思源,等.交叉分段PCA在锅炉故障诊断中的应用［J］.信息与控制,2020,49(4):507-512. WANG Wenbiao,TIAN Zhiyuan,WANG Siyuan,et al.Application of cross sectional PCA in boiler fault diagnosis ［J］.Information and Control,2020,49(4):507-512.
［2］冯珑.基于故障树的火电厂金属壁锅炉设备故障诊断方法研究［J］.世界有色金属,2016(11):39-40. FENG Long.Research on fault diagnosis method of metal wall boiler equipment in thermal power plant based on Fault Tree ［J］.World Nonferrous Metals,2016(11):39-40.
［3］SIMONE B,THUAN L,ONDREJ H,et al.Real-time monitoring energy efficiency and performance degradation of condensing boilers ［J］.Energy Conversion and Management,2017,136:329-339.
［4］ZHU Y,GENG L.Research on SDG fault diagnosis of ocean shipping boiler system based on fuzzy granular computing under data fusion ［J］.Polish Maritime Research,2018,25(S2):92-97.
［5］刘海桂.机械设备故障诊断与监测的常用方法及其发展趋势［J］.信息记录材料,2019,20(12):214-215. LIU Haigui.Common methods and development trend of mechanical equipment fault diagnosis and monitoring ［J］.Information Recording Materials,2019,20(12):214-215.
［6］牛培峰,张泽,王怀宝.基于模糊聚类神经网络的电站锅炉故障诊断研究［J］.微计算机信息,2010,26(7):40-42. NIU Peifeng,ZHANG Ze,WANG Huaibao.Study on fault diagnosis of power plant boiler based on fuzzy clustering neural network ［J］.Microcomputer Information,2010,26(7):40-42.
［7］许裕栗,张静,李柠,等.基于数据挖掘的锅炉在线运行状态监测［J］.热能动力工程,2019,34(2):82-87,115. XU Yuli,ZHANG Jing,LI Ning,et al.Online monitoring of boiler operation based on data mining ［J］.Thermal Power Engineering,2019,34(2):82-87,115.
［8］邱文严.基于小波神经网络火电厂锅炉故障诊断的仿真研究［J］.煤矿机械,2012,33(9):269-271. QIU Wenyan.Simulation research on boiler fault diagnosis of thermal power plant based on wavelet neural network ［J］.Coal Mine Machinery,2012,33(9):269-271.
［9］高鹤元,甘辉兵,郑卓,等.粒子群优化神经网络在船舶辅锅炉故障诊断中的应用［J］.计算机应用与软件,2020,37(8):137-141,148. GAO Heyuan,GAN Huibing,ZHENG Zhuo,et al.Application of particle swarm optimization neural network in fault diagnosis of marine auxiliary boiler ［J］.Computer Applications and Software,2020,37(8):137-141,148.
［10］姜继伟.循环流化床锅炉故障诊断专家系统研究［D］.青岛:中国石油大学（华东）,2009.
［11］石宁.基于模糊专家系统的锅炉故障诊断方法的研究［D］.沈阳:沈阳理工大学,2010.
［12］于大鹏,赵德有,汪玉.螺旋桨鸣音的混沌动力特性研究［J］.声学学报,2010,35(5):530-538. YU Dapeng,ZHAO Deyou,WANG Yu.Chaotic dynamic characteristics of propeller chirp ［J］.Acta Acoustics,2010,35(5):530-538.
［13］刘敏,范红波,张英堂,等.机械振动信号自适应多尺度非线性动力学特征提取方法研究［J］.振动与冲击,2020,39(14):224-232,250. LIU Min,FAN Hongbo,ZHANG Yingtang,et al.Research on adaptive multi-scale nonlinear dynamic feature extraction method of mechanical vibration signal ［J］.Vibration and Shock,2020,39(14):224-232,250.
［14］席乐乐.基于混沌理论的BCG信号非线性特性分析［D］.桂林:桂林电子科技大学,2019.
［15］付强,李晨溪,张朝曦.关于G-P算法计算混沌关联维的讨论［J］.解放军理工大学学报(自然科学版),2014,15(3):275-282. FU Qiang,LI Chenxi,ZHANG Chaoxi.Discussion on the calculation of chaos correlation dimension by G-P algorithm ［J］.Journal of PLA University of Science and Technology (Natural Science Edition),2014,15(3):275-282.
［16］韩雪琼.混沌系统的Lyapunov维数［D］.合肥:合肥工业大学,2017.
［17］吕金虎,陆君安,陈士华.混沌时间序列分析及其应用［M］.武汉:武汉大学出版社,2005.

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备注/Memo

备注/Memo:
收稿日期： 2020-10-19
* 基金项目： 国家自然科学基金重点项目（51534008）
作者简介： 潘伟，博士，副教授，主要研究方向为安全监测与预警技术。
更新日期/Last Update: 2021-01-08