基于传感器关联网络的燃气轮机异常检测方法

尹莉莉; 陈德运; 顾恒文; 王伟影

doi:10.13224/j.cnki.jasp.2018.01.005

基于传感器关联网络的燃气轮机异常检测方法

doi: 10.13224/j.cnki.jasp.2018.01.005

1.
哈尔滨理工大学软件学院,哈尔滨 150040
2.
哈尔滨理工大学计算机学院,哈尔滨 150080
3.
中国船舶重工集团公司第七〇三研究所燃气轮机事业部,哈尔滨 150001

基金项目: 黑龙江省自然科学基金面上资助项目（F201626）

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出版历程
- 收稿日期: 2016-07-22
- 刊出日期: 2018-01-28

Anomaly detection method based on gas turbine sensor associated network

摘要

摘要: 通过分析燃气轮机4种典型异常形式的产生机理以及特征表现,得到了不同异常形式与传感器关联网络结构特征之间的映射关系。在此基础上,得到传感器关联网络的异常特征模式,提出了基于传感器关联网络的燃气轮机异常检测策略。通过实例分析,证明了融合多源信息基础上建立的传感器关联网络模型,可以过滤掉节点间关联指标低于阈值037的相关性,有效的实现燃气轮机的稳态异常检测,并可以检测出转速上升过程中,节点间相关性的正常线性变化趋势,存在大于12%的异常凹陷非线性趋势,从而有效的实现燃气轮机的动态异常检测。
- 故障诊断 /
- 稳态异常检测 /
- 燃气轮机 /
- 动态异常检测 /
- 传感器关联网络
Abstract: The mechanism and characteristics of four typical abnormal forms of gas turbine were analyzed, and the mapping between the different abnormal features and the sensor network characteristics were acquired. On this basis, the abnormal characteristics of sensor network were obtained, and the detection strategy of gas turbine anomaly based on sensor correlation network was proposed. The results of experiments indicated that sensor related network model based on multi source information fusion could filter out the correlation between nodes when the correlation indicator was below the threshold of 037, and achieve steadystate anomaly detection of gas turbine effectively, showing that there was a nonlinear trend of abnormal depression greater than 12% during the speed up; although the correlation between nodes under normal circumstances should be a linear trend, this method could achieve dynamic anomaly detection of gas turbine effectively.
- fault diagnosis /
- steadystate anomaly detection /
- gas turbine /
- dynamic anomaly detection /
- sensor association network

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参考文献(17)

[1]	RAHME S,MESKIN N.Adaptive sliding mode observer for sensor fault diagnosis of an industrial gas turbine［J］.Control Engineering Practice,2015,38:57-74.
[2]	GAO Z,CECATI C,DING S X.A survey of fault diagnosis and faulttolerant techniques:Part Ⅰ fault diagnosis with modelbased and signalbased approaches［J］.IEEE Transactions on Industrial Electronics,2015,62(6):3757-3767.
[3]	薛银春,孙健国.燃气轮机控制技术综述［J］.航空动力学报,2005,20(6):1066-1071.XUE Yinchun,SUN Jianguo.A survey of gas turbine control technique［J］.Journal of Aerospace Power,2005,20(6):1066-1071.(in Chinese)
[4]	朴文哲.燃气轮机故障诊断技术的研究与展望［J］.科技创新与应用,2015(2):101.
[5]	TAYARANIBATHAIE S S,KHORASANI K.Fault detection and isolation of gas turbine engines using a bank of neural networks［J］.Journal of Process Control,2015,36(1):22-41.
[6]	LOBODA I,OLIVARESROBLES M A.Gas turbine fault diagnosis using probabilistic neural networks［J］.International Journal of Turbo and JetEngines,2015,32(2):175-191.
[7]	ILYAS M,MAHGOUB I.Smart dust:sensor network applications,architecture and design［M］.Boca Raton,FL:CRC Press,2016.
[8]	KILIC G,UNLUTURK M S.Testing of wind turbine towers using wireless sensor network and accelerometer［J］.Renewable Energy,2015,75:318-325.
[9]	DERVILIS N,CHOI M,TAYLOR S G,et al.On damage diagnosis for a wind turbine blade using pattern recognition［J］.Journal of Sound and Vibration,2014,333(6):1833-1850.
[10]	DI VINCENZO G,MAROCCHINI F P,SINGH B.Vane position sensor installation within a turbine case:US 20160123844［P］.2016-05-05.
[11]	LU F,CHEN Y,HUANG J,et al.An integrated nonlinear modelbased approach to gas turbine engine sensor fault diagnostics［J］.Journal of Aerospace Engineering,2014,228(11):2007-2021.
[12]	陈立伟,王铁深,黄璐.基于EMD能量熵和相关向量机的燃机涡轮叶片故障诊断方法［J］.应用科技,2016,43(1):67-71.CHEN Liwei,WANG Tieshen,HUANG Lu.Gas turbine blade s fault diagnosis method based on EMD energy entropy and relevance vector machine［J］.Applied Science and Technology,2016,43(1):67-71.(in Chinese)
[13]	徐启华,师军.基于支持向量机的航空发动机故障诊断［J］.航空动力学报,2005,20(2):298-302.XU Qihua,SHI Jun.Aeroengine fault diagnosis based on support vector machine［J］.Journal of Aerospace Power,2005,20(2):298-302.(in Chinese)
[14]	TOLANI D K,YASAR M,RAY A,et al.Anomaly detection in aircraft gas turbine engines［J］.Journal of Aerospace Computing,Information,and Communication,2006,3(2):44-51.
[15]	王平,廖明夫.滚动轴承故障诊断的自适应共振解调技术［J］.航空动力学报,2005,20(4):606-612.WANG Ping,LIAO Mingfu.Adaptive demodulated resonance technique for the rolling bearing fault diagnosis［J］.Journal of Aerospace Power,2005,20(4):606-612.(in Chinese)
[16]	WONG P K,YANG Z,VONG C M,et al.Realtime fault diagnosis for gas turbine generator systems using extreme learning machine［J］.Neurocomputing,2014,128(5):249-257.
[17]	廖明夫,马振国,邓巍.某型航空发动机中介轴承外环故障振动分析［J］.航空动力学报,2011,26(11):2422-2426.LIAO Mingfu,MA Zhenguo,DENG Wei.Vibration analysis on turbofan engine intershaft bearing with outer race defect［J］.Journal of Aerospace Power,2011,26(11):24222426.(in Chinese)