基于MAF的传感器故障检测与诊断

doi:10.11949/j.issn.0438-1157.20141595

化工学报 ›› 2015, Vol. 66 ›› Issue (5): 1831-1837.DOI: 10.11949/j.issn.0438-1157.20141595

基于MAF的传感器故障检测与诊断

付克昌¹, 袁世辉², 蒋世奇¹, 朱明¹, 沈艳¹

1 成都信息工程学院控制工程学院, 四川成都 610025;
2 中国燃气涡轮研究院, 四川江油 621703

收稿日期:2014-10-23 修回日期:2015-01-30 出版日期:2015-05-05 发布日期:2015-05-05
通讯作者: 付克昌
基金资助:
航空科学基金项目(201210P8003)、四川省应用基础研究项目(2014JY0257);四川省科技厅科技支撑计划项目(2014GZ0009);四川省教育厅自然科学重点项目(14ZA0171)和四川省教育厅青年基金项目(11ZB087)。

Sensor fault detection and diagnosis using MAF

FU Kechang¹, YUAN Shihui², JIANG Shiqi¹, ZHU Ming¹, SHEN Yan¹

1 School of Control Engineering, Chengdu University of Information Technology, Chengdu 610225, Sichuan, China;
2 China Gas Turbine Establishment, Jiangyou 621703, Sichuan, China

Received:2014-10-23 Revised:2015-01-30 Online:2015-05-05 Published:2015-05-05
Supported by:
supported by Aeronautical Science Foundation of China (201210P8003), Sichuan Science Fund for Applied Basic Research (2014JY0257), Scientific Research Fund of Sichuan Provincial Science & Technology Department (2014GZ0009), Scientific Reserch Fund of Sichuan Provincial Education Department (14ZA0171) and Sichuan Province Education Department Youth Fund Research Projects (11ZB087).

摘要/Abstract

摘要： 针对工业控制系统中变量之间既存在线性相关性,且在时间结构上呈现自相关的特点,提出了一种基于最小/最大自相关因子(min/max autocorrelation factors, MAF)分析的传感器故障检测与诊断方法。首先,利用正常工况下的历史数据进行自相关因子分析,获得强自相关因子和弱自相关因子;在此基础上构造故障检测统计量,由核密度估计方法获得故障检测控制限,根据贡献图进行传感器故障定位。将所提出的方法应用于连续反应釜仿真过程的传感器故障检测与诊断,与经典的多变量统计方法——主元分析方法相比,所提出的方法能避免虚警,更快地检测缓变故障,并能更好地诊断和解释复杂故障。

Abstract: For industrial processes, there are not only correlations among variables, but also autocorrelation in temporal structure of these variables, therfore, a new sensor fault detection and diagnosis method based on min/max autocorrelation factors (MAF) was proposed in this work. Firstly, MAF analysis of historical normal data was made. Then, strong autocorrelation factors and weak autocorrelation factors were obtained. Based on these factors, the statistics for fault detection were constructed and corresponding contribution plots were derived. The proposed method was applied to the continuous stirred tank reactor (CSTR) and compared with the principal component analysis method. Simulation results demonstrated that the proposed method could detect sensor faults with slow variation more quickly with less false-alarm. The contribution plots based on MAF can explain complicated sensor fault more reasonably than principal component analysis (PCA), which is a classical multivariate statistical method for process monitoring.

Key words: min/max autocorrelation factors, principal component analysis, process systems, sensor fault diagnosis, algorithm

中图分类号:

TP277

付克昌, 袁世辉, 蒋世奇, 朱明, 沈艳. 基于MAF的传感器故障检测与诊断[J]. 化工学报, 2015, 66(5): 1831-1837.

FU Kechang, YUAN Shihui, JIANG Shiqi, ZHU Ming, SHEN Yan. Sensor fault detection and diagnosis using MAF[J]. CIESC Journal, 2015, 66(5): 1831-1837.

参考文献 24

[1]	Mehranbod N. A probabilistic approach for sensor fault detection and identification [D]. Philadelphia, USA: Drexel University, 2002
[2]	Hu Y P, Chen H X, Xie J L, Yang X S, Zhou C. Chiller sensor fault detection using a self-adaptive principal component analysis method [J]. Energy and Buildings, 2012, 54: 252-258
[3]	Fu Kechang (付克昌), Dai Liankui (戴连奎), Wu Tiejun (吴铁军). Sensor fault diagnosis approach based on structure optimized SRAMS [J]. Journal of Chemical Industry and Engineering (China) (化工学报), 2007, 58 (8): 2045-2050
[4]	Youssef A B, Khil S K, I. Slama-Belkhodja. State observer-based sensor fault detection and isolation, and fault tolerant control of a single-phase PWM rectifier for electric railway traction [J]. IEEE transactions on Power Electronics, 2013, 28 (12): 5842-5853
[5]	Jafar Z, Ehsan S. Robust sensor fault detection based on nonlinear unknown input observer [J]. Measurement, 2014, 48: 355-367
[6]	Jia Runda (贾润达), Mao Zhizhong (毛志忠), Wang Fuli (王福利). KPLS model based product quality control for batch processes [J]. CIESC Journal (化工学报), 2013, 64 (4): 1332-1339
[7]	Pei X D, Yamashita Y, Yoshida M, Matsumoto S. Discriminant analysis and control chart for the fault detection and identification [J]. Computer Aided Chemical Engineering, 2006, 21: 1281-1286
[8]	Hu Yunpeng (胡云鹏), Chen Huanxin (陈焕新), Zhou Cheng (周诚), Yang Xiaoshuang (杨小双),Xu Rongji (徐荣吉). Analysis of sensor fault detection in chiller based on PCA method [J]. CIESC Journal (化工学报), 2012, 63 (S2): 85-88
[9]	Jinane H, Claude D, Demba D. Incipient fault detection and diagnosis based on Kullback-Leibler divergence using principal component analysis: (Ⅰ) [J]. Signal Processing, 2014, 94 (1): 278-287
[10]	Garcia-Alvarez D, Fuente M J, Sainz G I. Fault detection and isolation in transient states using principal component analysis[J]. Journal of Process Control, 2012, 22 (3): 551-563
[11]	Deng P C, Gui W H, Xie Y F. Latent space transformation based on principal component analysis for adaptive fault detection [J]. Control Theory & Applications, IET, 2010, 4 (11): 2527-2538
[12]	He Ning (何宁). Researches on performance monitoring and fault diagnosis for process industry based on ICA-PCA technique [D]. Hangzhou: Zhejiang University, 2004
[13]	Shi H T, Liu J C, Xue P, Zhang K, Wu Y H, Zhang L X, Tan S. Improved relative-transformation principal component analysis based on mahalanobis distance and its application for fault detection [J]. Acta Automatica Sinica, 2013, 39 (9): 1533-1542
[14]	Wang S W, Cui J T. Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method [J]. Applied Energy, 2005, 82 (3): 197-213
[15]	Joydeb Mukherjee, Venkataramana B Kini, Sunil Menon, Lalitha Eswara. Gas turbine fault detection and diagnosis using nonlinear feature extraction methods//ASME Turbo Expo 2005: Power for Land, Sea, and Air [C]. Reno, Nevada, USA, 2005, 9 (1): 737-743
[16]	Uluyol O, Kyusung Kim, Nwadiogbu E O. Synergistic use of soft computing technologies for fault detection in gas turbine engines [J]. Systems, Man, and Cybernetics, Part C: IEEE Transactions on, Applications and Reviews, 2006, 36 (4): 476-484
[17]	Switzer P, Green A A. Min/max autocorrelation factors for multivariate spatial imagery [R]. Stanford University, Tech. Report No.6, Department of Statistics, 1984
[18]	Nielsen A A. Kernel maximum autocorrelation factor and minimum noise fraction transformations [J]. IEEE Transactions on Image Processing, 2011, 20 (3): 612-624
[19]	Woillez M, Rivoirard J, Petitgas P. Using min/max autocorrelation factors of survey-based indicators to follow the evolution of fish stocks in time [J]. Aquatic Living Resources, 2009, 22 (2): 193-200
[20]	Kresta J, MacGregor, J F, Marlin, T E. Multivariate statistical monitoring of process operating performance [J]. Canadian Journal of Chemical Engineering, 1991, 69: 35-47
[21]	Bowman A W, Azzalini A. Applied Smoothing Techniques for Data Analysis [M]. Oxford: Oxford University Press, 1997
[22]	Bowman A W. An alternative method of cross-validation for the smoothing of density estimates [J]. Biometrika, 1984, 71 (2): 353-360
[23]	Li G, Qin S Z, Ji Y D, Zhou D H. Total PLS based contribution plots for fault diagnosis [J]. Acta Automatica Sinica, 2009, 35 (6): 759-761
[24]	Luyhen W. Process Modeling, Simulation, and Control for Chemical Engineers [M]. New York: McGraw-Hill, 1988

基于MAF的传感器故障检测与诊断

Sensor fault detection and diagnosis using MAF

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