|本期目录/Table of Contents|

[1]金 典,谢 珊*,丁 军,等.核电站主蒸汽系统冗余压力传感器异常检测[J].厦门大学学报(自然科学版),2019,58(04):582-588.[doi:10.6043/j.issn.0438-0479.201811018]
 JIN Dian,XIE Shan*,DING Jun,et al.Fault detection for redundant pressure sensors in main steam system in nuclear power plants[J].Journal of Xiamen University(Natural Science),2019,58(04):582-588.[doi:10.6043/j.issn.0438-0479.201811018]
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《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
58卷
期数:
2019年04期
页码:
582-588
栏目:
研究论文
出版日期:
2019-07-28

文章信息/Info

Title:
Fault detection for redundant pressure sensors in main steam system in nuclear power plants
文章编号:
0438-0479(2019)04-0582-07
作者:
金 典1谢 珊1*丁 军1吴一纯1李 宁1曹培根2
1.厦门大学能源学院,福建 厦门 361102; 2.福清核电站,福建 福州 350300
Author(s):
JIN Dian1XIE Shan1*DING Jun1WU Yichun1LI Ning1CAO Peigen2
1.College of Energy,Xiamen University,Xiamen 361102,China; 2.Fuqing Nuclear Power Plant,Fuzhou 350300,China
关键词:
独立成分分析 序贯概率比检验 核电站 冗余传感器校准 异常检测
Keywords:
independent component analysis(ICA) sequential probability ratio test(SPRT) nuclear power plant redundant sensors calibration fault detection
分类号:
TL 362.3
DOI:
10.6043/j.issn.0438-0479.201811018
文献标志码:
A
摘要:
为保障核电站运行安全,电站设置冗余传感器对重要系统的关键参数进行测量,传感器的健康状况将直接影响测量结果.在被测冗余传感器个数较少,为3个左右时,目前核电站常用的传感器校准方法很难区分故障传感器.将独立成分分析和序贯概率比检验(independent component analysis-sequential probability ratio test,ICA-SPRT)相结合的方法用于冗余传感器故障检测,使用核电站主蒸汽系统冗余压力传感器数据进行验证.与简单平均法的结果进行对比可知,该方法在冗余度不高的情况下明显优于简单平均法,能及时准确地检测到传感器信号的漂移.
Abstract:
Redundant sensors are installed for measurement of critical parameter in crucial systems in nuclear power plants(NPPs)to ensure the operation safety,and status of the sensor will directly influence the results of measurements.Now it is difficult for traditional sensor calibration methods(simple average,instrumentation and calibration monitoring program,parity space,etc.)commonly used in NPPs to recognize the faulty redundant sensors when redundancy of systematic parameter is 3 or low.In this article,independent component analysis combined with sequential probability ratio test(ICA-SPRT)is proposed for fault detection in redundant sensors,and is validated by the dataset from redundant pressure sensors in main steam system in NPPs.Compared with results of simple average,the method cannot only outperform traditional ones but also accurately detects the drift of sensors’ signal in time and provides more time for fault analysis and maintenance of sensors.

参考文献/References:

[1] DAVIS E,FUNK D,HOOTEN D.On-line monitoring of instrument channel performance:TR-104965[R].California:EPRI,1998.
[2] HINES J W,SEIBERT R.Technical review of on-line monitoring techniques for performance assessment:NUREG-6895[R].Washington DC:Nuclear Regulatory Commission,2006.
[3] JUTTEN C,HERAULT J.Independent component analysis versus principal component analysis[J].Signal Processing IV,Theories and Applications,1988,12(3):643-646.
[4] BELL A J,SEJNOWSKI T J.An information-maximization approach to blind separation and blind deconvolution[J].Neural Computation,1995,7(6):1129-1159.
[5] LEE T W,GIROLAMI M,SEJNOWSKI T J.Independent component analysis using an extended infomax algorithm for subgaussian and supergaussian sources[J].Neural Computation,1999,11(2):417-441.
[6] AMARI S I.Natural gradient works efficiently in learning[J].Neural Computation,1998,10(2):251-276.
[7] HYV?RINEN A.Survey on independent component analysis[J].Neural Computing Surveys,1999,2:94-128.
[8] HYV?RINEN A,OJA E.A fast fixed-point algorithm for independent component analysis[J].Neural Computation,1997,9(7):1483-1492.
[9] JUTTEN C,HERAULT J.Blind separation of sources,part Ⅰ:an adaptive algorithm based on neuromimetic archi-tecture[J].Signal Processing,1991,24(1):1-10.
[10] KARHUNEN J,HYVARINEN A,VIGARIO R,et al.Applications of neural blind separation to signal and image processing[C]∥Proceeding of the IEEE 1997 International Conference on Acoustics,Speech,and Signal Processing.Munich:IEEE,1997:131-134.
[11] STONE J V,PORRILL J,POTER N R,et al.Spatio-temporal ICA of fMRI data[R/OL].[2018-11-01].citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.32.3892&rep=rep1&type=pdf.
[12] BELL A J,SEJNOWSKI T J.The "independent components" of natural scenes are edge filters[J].Vision Research,1997,37(23):3327-3338.
[13] YUEN P C,LAI J H.Face representation using independent component analysis[J].Pattern Recognition,2002,35(6):1247-1257.
[14] ROBERTS S,EVERSON R.Independent component analysis,principles and practice[M].Cambridge:Cambri-dge University Press,2001:338-340.
[15] DING J,HINES J W,RASMUSSEN B.Independent component analysis for redundant sensor validation[C]∥Proceedings of the 2003 Maintenance and Reliability Conference.Knoxville:MARCON,2003:4-7.
[16] DING,J,GRIBOK A,HINES J W,et al.Redundant sensor calibration monitoring using ICA and PCA[J].Real Time Systems Special Issue on "Applications of Intelligent Real-Time Systems for Nuclear Engineering",2004,27(1):27-48.
[17] CHEN H T,JIANG B,LU N Y,et al.Real-time incipient fault detection for electrical traction systems of CRH2[J].Neurocomputing,2018,306:119-129.
[18] FENG L,DI T R,ZHOU Y W.HSIC-based kernel independent component analysis for fault monitoring[J].Chemometrics and Intelligent Laboratory Systems,2018,178:47-55.
[19] HYV?RINEN A.Fast and robust fixed-point algorithms for independent component analysis[J].IEEE Transactions on Neural Networks,1999,10(3):626-634.
[20] HYV?RINEN A,KARHUNEN J,OJA E.Independent component analysis[M].Hoboken:Wiley-Interscience,2001:1-11,125-137.
[21] DING J,GRIBOK A V,HINES J W,et al.Redundant sensor calibration monitoring using independent component analysis and principal component analysis[J].Real-Time Systems,2004,27(1):27-47.
[22] WALD A.Sequential tests of statistical hypotheses[J].The Annals of Mathematical Statistics,1945,16(2):117-186.
[23] WALD A.Sequential analysis[M].New York:Wiley,1947:15-23.
[24] CHARLESWORTH J P,TEMPLE J A G.Engineering applications of ultrasonic time-of-flight diffraction[M].London:Research Studies Press,1989:47-62.
[25] LORDEN G.2-SPRT’S and modified kiefer-weiss problem of minimizing an expected sample size[J].The Annals of Statistics,1976,4(2):281-291.
[26] YU C G,SU B J.A non-parametric sequential rank-sum probability ratio test method for binary hypothesis testing[J].Signal Processing,2004,84(7):1267-1272.
[27] ARMITAGE P.Sequential analysis with more than two alternative hypotheses,and its relation to discriminant function analysis[J].Journal of the Royal Statistical Society:Series B(Methodological),1950,12(1):137-144.
[28] 黄寒砚,王磊.基于参数优化的截尾序贯检验法[J].飞行器测控学报,2011,30(3):49-55.
[29] 张志华,刘海涛.广义计数型序贯抽样检验[J].海军工程大学学报,2011,23(6):44-48.
[30] GAO Y,LIU Y,LI X R.Tracking-aided classification of targets using multihypothesis sequential probability ratio test[J].IEEE Transactions on Aerospace and Electronic Systems,2017,54(1):233-245.
[31] GOLZ M,FAUSS M,ZOUBIR A.A bootstrapped sequ-ential probability ratio test for signal processing applica-tions[C]∥IEEE International Workshop on Computa-tional Advances in Multi-sensor Adaptive Processing.Pisca-taway:IEEE,2018:10-13.
[32] LIU K P,ZENG Q H.An improved sequential probability ratio test method for residual test[J].Electronics Optics & Control,2009,16(8):36-39.
[33] DAVIS E,FUNK D,HOOTEN D.On-line monitoring of instrument channel performance TR-104965[R].Cali-fornia:EPRI,1998.

备注/Memo

备注/Memo:
收稿日期:2018-11-11录用日期:2019-02-18
基金项目:厦门大学能源学院发展基金(2017NYFZ01)
*通信作者:shanxie@xmu.edu.cn
更新日期/Last Update: 1900-01-01