燃气轮机排气温度异常检测及诊断
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摘要
针对燃气轮机运行过程中的健康维护问题,提出了一种基于模糊C均值聚类的燃气轮机排气温度异常检测方法。以某型工业燃气轮机为例,采用模糊C均值聚类算法对排气温度的监测数据进行了聚类分析,得到了燃气轮机不同运行状态下的排气温度特征模式,并在此基础上实现了燃气轮机排气温度异常状态下的故障诊断分析。研究结果表明,燃气轮机甩负荷及其热通道部件的损坏失效均能够对排气温度产生不同程度的影响,模糊C均值聚类算法可以有效实现燃气轮机排气温度的异常检测,为燃气轮机性能退化预测及故障诊断提供决策参考。
Aiming at the problem of health maintenance under gas turbine operating conditions,a fuzzy C-means clustering approach was applied to realize the anomaly detection of gas turbine exhaust gas temperature in this paper. Taking an industry gas turbine as an example,the clustering analysis of exhaust gas temperature based on fuzzy C-means clustering approach was studied to obtain the feature pattern of exhaust gas temperature when the gas turbine was in different operating conditions. Based on this,a diagnosis study was carried out to analyze the exhaust gas temperature in the abnormal state. The results showed that both the gas engine's load rejection and the damage failure of hot section parts have effects on the exhaust gas temperature of the gas turbine to varying degrees. The fuzzy C-means clustering approach can effectively realize the anomaly detection of gas turbine exhaust gas temperature,which provides decision references for gas turbine performance degradation prediction and fault diagnosis.
Aiming at the problem of health maintenance under gas turbine operating conditions,a fuzzy C-means clustering approach was applied to realize the anomaly detection of gas turbine exhaust gas temperature in this paper. Taking an industry gas turbine as an example,the clustering analysis of exhaust gas temperature based on fuzzy C-means clustering approach was studied to obtain the feature pattern of exhaust gas temperature when the gas turbine was in different operating conditions. Based on this,a diagnosis study was carried out to analyze the exhaust gas temperature in the abnormal state. The results showed that both the gas engine's load rejection and the damage failure of hot section parts have effects on the exhaust gas temperature of the gas turbine to varying degrees. The fuzzy C-means clustering approach can effectively realize the anomaly detection of gas turbine exhaust gas temperature,which provides decision references for gas turbine performance degradation prediction and fault diagnosis.
引文
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[15]PAL N R,BEZDEK J C.On cluster validity for the fuzzy Cmeans model[J].IEEE Transactions on Fuzzy Systems,1995,3(3):370-379.
[16]CHATTOPADHYAY S.A comparative study of fuzzy cmeans algorithm and entropy-based fuzzy clustering algorithms[J].Computing and Informatics,2011,30:701-720.
[17]毛华军.燃气轮机排气温度监视和保护功能分析[J].华电技术,2009,31(8):11-15.MAO Huajun.Analysis of exhaust temperature monitor and protection function for gas turbine[J].Huadian Technology,2009,31(8):11-15.
[2]LI Y G.Performance analysis based gas turbine diagnostics:a review[J].Proceedings of the Institution of Mechanical Engineers,Part A:Journal of Power and Energy,2002,216(5):363-377.
[3]HE H X,LI N,ZHENG G F,et al.Anomaly detection based on multi-detector fusion used in turbine[J].Journal of Harbin Institute of Technology,2013,20(1):113-117.
[4]PATRICK H P E,HA D.Exhaust gas temperature capabilities now in system 1software[J].Product Update,2005,25(1):88-89.
[5]LIU X,JEFFRIES J B,HANSON R K,et al.Development of a tunable diode laser sensor for measurements of gas turbine exhaust temperature[J].Applied Physics B,2006,82(3):469-478.
[6]宋云雪,张科星,史永胜.基于多元线性回归的发动机性能参数预测[J].航空动力学报,2009,24(2):427-431.SONG Yunxue,ZHANG Kexing,SHI Yongsheng.Research on aeroengine performance parameters forecast based on multiple linear regression forecasting method[J].Journal of Aerospace Power,2009,24(2):427-431.
[7]汪雪飞,杨建明.PG6551B燃机排气分散度大故障的分析与处理[J].燃气轮机技术,2004,17(2):58-61.WANG Xuefei,YANG Jianming.Analysis and treatment of larger exhaust dispersity fault for PG6551B gas turbine[J].Gas Turbine Technology,2004,17(2):58-61.
[8]陈娇,王永泓,翁史烈.广义回归神经网络在燃气轮机排气温度传感器故障检测中的应用[J].中国电机工程学报,2009,29(32):92-97.CHEN Jiao,WANG Yonghong,WENG Shilie.Application of general regression neural network in fault detection of exhaust temperature sensors on gas turbines[J].Proceedings of the CSEE,2009,29(32):92-97.
[9]MUTHURAMAN S,TWIDDLE J,SINGH M,et al.Condition monitoring of SSE gas turbines using artificial neural networks[J].Insight-Non-Destructive Testing and Condition Monitoring,2012,54(8):436-439.
[10]DUNN J.A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters[J].Journal of Cybernetics,1973,8(3):32-57.
[11]BEZDEK J C.Pattern recognition with fuzzy objective function algorithms[M].New York:Plenum Press,1981:62-78.
[12]赵欢,王培红,钱瑾,等.基于模糊C-均值聚类的锅炉监控参数基准值建模[J].中国电机工程学报,2011,11(15):16-22.ZHAO Huan,WANG Peihong,QIAN Jin,et al.Modeling for target-value of boiler monitoring parameters based on fuzzy C-means clustering algorithm[J].Proceedings of the CSEE,2011,11(15):16-22.
[13]徐超,张培林,任国全,等.基于改进半监督模糊C-均值聚类的发动机磨损故障诊断[J].机械工程学报,2011,47(17):55-60.XU Chao,ZHANG Peilin,REN Guoquan,et al.Engine wear fault diagnosis based on improved semi-supervised fuzzy C-means clustering[J].Journal of Mechanical Engineering,2011,47(17):55-60.
[14]YANG Y,HUANG S.Image segmentation by fuzzy Cmeans clustering algorithm with a novel penalty term[J].Computing and Informatics,2007,26:17-31.
[15]PAL N R,BEZDEK J C.On cluster validity for the fuzzy Cmeans model[J].IEEE Transactions on Fuzzy Systems,1995,3(3):370-379.
[16]CHATTOPADHYAY S.A comparative study of fuzzy cmeans algorithm and entropy-based fuzzy clustering algorithms[J].Computing and Informatics,2011,30:701-720.
[17]毛华军.燃气轮机排气温度监视和保护功能分析[J].华电技术,2009,31(8):11-15.MAO Huajun.Analysis of exhaust temperature monitor and protection function for gas turbine[J].Huadian Technology,2009,31(8):11-15.