[1]马洁,党爱民,李刚,等.基于MSPM的故障诊断技术研究现状与展望[J].华侨大学学报(自然科学版),2012,33(6):601-607.[doi:10.11830/ISSN.1000-5013.2012.06.0601]
　MA Jie,DANG Ai-min,LI Gang,et al.Research Status and Prospect of Fault Diagnosis Technology Based on MSPM[J].Journal of Huaqiao University(Natural Science),2012,33(6):601-607.[doi:10.11830/ISSN.1000-5013.2012.06.0601]
点击复制

基于MSPM的故障诊断技术研究现状与展望()
分享到：

《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第33卷
期数:
2012年第6期
页码:
601-607
栏目:
出版日期:
2012-11-20

文章信息/Info

Title:
Research Status and Prospect of Fault Diagnosis Technology Based on MSPM
文章编号:
1000-5013(2012)06-0601-07
作者:
马洁1;  党爱民2;  李刚3;  周东华3
1. 北京信息科技大学 自动化学院, 北京 100192;2. 洛阳双瑞精铸钛业有限公司, 河南 洛阳 471003;3. 清华大学 自动化系, 北京 100084
Author(s):
MA Jie1;  DANG Ai-min2;  LI Gang3;  ZHOU Dong-hua3
1. Automation College, Beijing Information Science & Technology University, Beijing 100192, China; 2. Luoyang Sunrui Titanium Precision Casting Co Ltd, Luoyang 471003, China; 3. Department of Automation, Tsinghua University, Beijing 100084, China
关键词:
多元统计过程监控;  故障诊断;  故障预测;  主元分析;  偏最小二乘法;  独立分量分析
Keywords:
multivariate statistical process monitoring;  fault diagnosis;  fault prediction;  principal component analysis;  partial least squares;  independent component analysis
分类号:
TP277
DOI:
10.11830/ISSN.1000-5013.2012.06.0601
文献标志码:
A
摘要:
首先,阐述基于主元分析(PCA)模型、偏最小二乘法(PLS)模型和独立分量分析(ICA)模型的统计过程监控方法的基本思想及应用情况,并综述各种方法的研究现状及发展趋势.其次,介绍将传统统计过程监控技术与故障预测技术相结合,并实现基于多元统计过程监控(MSPM)的故障预测的方法及其研究成果.最后,分别就多元故障预测技术中出现的非高斯、非线性、多模态、概率分布、间歇过程的故障预测和应用验证等6个难点问题进行讨论.
Abstract:
First of all, this paper introduces the basic ideas and applications of statistical process monitoring method based on principal component analysis(PCA)model, partial least squares(PLS)model and independent component analysis(ICA)model. The present research situation and development trend about various methods are reviewed. Secondly, by combining fault prediction technology with the traditional statistical process monitoring technology, fault prediction method based on multivariate statistical process monitoring(MSPM)can be realized. And some research results are also introduced. Finally, six difficult problems in multivariate failure prediction technology such as non-Gaussian, non-linear, multi-modal. probability distribution, intermittent process fault prediction and application verification are discussed respectively.

参考文献/References:

[1] QIN S J.Statistical process monitoring: Basics and beyond[J].Journal of Chemometrics,2003,17(8/9):480-502.
[2] 周东华,胡艳艳.动态系统的故障诊断技术[J].自动化学报,2009,35(6):748-758.
[3] WISE B M,RICKER N L,VELTKAMP D F,et al.A theoretical basis for the use of principal component models for monitoring multivariate processes[J].Process Control and Quality,1990,1(1):41-51.
[4] DUNIA R,QIN S J.Joint diagnosis of process and sensor faults using principle component analysis[J].Control Engineering Practice,1998,6(4):457-469.
[5] WANG Hai-qing,SONG Zhi-huan,LI Ping.Fault detection behavior and performance analysis of PCA-based process monitoring methods[J].Ind Eng Chem Res,2002,41(10):2455-2464.
[6] MACGREGOR J F,JAECKLE C,KOPARISSIDES C,et al.Process monitoring and diagnosis by multiblock PLS methods[J].AIChE Journal,1994,40(5):826-838.
[7] CHOI S W,LEE I B.Multiblock PLS-based localized process diagnosis[J].Journal of Process Control,2005,15(3):295-306.
[8] CHEN Tao,SUN Yue.Probabilistic contribution analysis for statistical process monitoring: A missing variable approach[J].Control Engineering Practice,2009,17(4):469-477.
[9] WESTERHUIS J A,GURDEN S P,SMILDE A K.Generalized contribution plots in multivariate statistical process monitoring[J].Chemometrics and Intelligent Laboratory Systems,2000,51(1):95-114.
[10] DUNIA R,QIN S J.Subspace approach to multidimensional fault identification and reconstruction[J].AIChE Journal,1998,44(8):1813-1831.
[11] YUE H H,QIN S J.Reconstruction-based fault identification using a combined index[J].Industrial and Engineering Chemistry Research,2001,40(20):4403-4414.
[12] ALCALA C, QIN S J.Reconstruction-based contribution for process monitoring[J].Automatica,2009,45(7):1593-1600.
[13] KU W,STORER R H,GEORGAKIS C.Disturbance detection and isolation by dynamic principal component analysis[J].Chemometrics and Intelligent Laboratory Systems,1995,30(1):179-196.
[14] WANG Xun,KRUGER U,IRWIN G W,et al.Nonlinear PCA with the local approach for diesel engine fault detection and diagnosis[J].IEEE Transactions on Control Systems Technology,2008,16(1):122-129.
[15] LI Y,KURFESS T R,LIANG S Y.Stochastic prognostics for rolling element bearings[J].Mechanical Systems and Signal Processing,2000,14(5):747-762.
[16] ZHAO Shi-jian,ZHANG Jie,XU Yong-mao.Monitoring of processes with multiple operating modes through multiple principle component analysis models[J].Industrial and Engineering Chemistry Research,2004,43(22):7025-7035.
[17] CHEN Jung-hui,LIU Jia-lin.Mixture principal component analysis models for process monitoring[J].Industrial and Engineering Chemistry Research,1999,38(4):1478-1488.
[18] BAKSHI B R.Multiscale PCA with application to multivariate statistical process monitoring[J].AIChE Journal,1998,44(7):1596-1610.
[19] QIN S J,VALLE S,PIOVOSO M J.On unifying multiblock analysis with application to decentralized process monitoring[J].Journal of Chemometrics,2001,15(9):715-742.
[20] NOMIKOS P,MACGREGOR J F.Monitoring batch processes using multiway principal component analysis[J].AIChE Journal,1994,40(8):1361-1375.
[21] LEE J M,YOO C K,LEE I B.Fault detection of batch processes using multiway kernel principal component analysis[J].Computers and Chemical Engineering,2004,28(9):1837-1847.
[22] CHOI S W,MORRIS J,LEE I B.Dynamic model-based batch process monitoring[J].Chemical Engineering Science,2008,63(3):622-636.
[23] DOAN X,SRINIVASAN R.Online monitoring of multi-phase batch processes using phase-based multivariate statistical process control[J].Computers and Chemical Engineering,2008,32(1/2):230-243.
[24] YAO Yuan,GAO Fu-rong.A survey on multistage/multiphase statistical modeling methods for batch processes[J].Annual Reviews in Control,2009,33(2):172-183.
[25] YAO Yuan,GAO Fu-rong.Phase and transition based batch process modeling and online monitoring[J].Journal of Process Control,2009,19(5):816-826.
[26] WOLD H.Path models with latent variables: The NIPALS approach[M].Quantitative Sociology: International perspectives on mathematical and statistical modeling.New York:Academic Press,1975:307-357.
[27] MACGREGOR J F,JAECKLE C,KIPARISSIDES C,et al.Process monitoring and diagnosis by multiblock PLS methods[J].AIChE Journal,1994,40(5):826-838.
[28] KOURTI T,NOMIKOS P,MACGREGOR J F.Analysis,monitoring and fault diagnosis of batch processes using multiblock and multiway PLS[J].Journal of Process Control,1995,5(4):277-277.
[29] LI Gang,QIN Si-zhao,JI Yin-dong,et al.Total PLS based contribution plots for fault diagnosis[J].ACTA Automatica Sinica,2009,35(6):759-765.
[30] QIN S J,MCAVOY T J.Nonlinear PLS modeling using neural network[J].Computers and Chemical Engineering,1992,16(4):379-391.
[31] HELLAND K,BERNTSEN H E,BORGEN O,et al.Recursive algorithm for partial least squares regression[J].Chemometrics and intelligent laboratory systems,1992,14(1/2/3):129-137.
[32] ODGAARD P,LIN B,JORGENSEN S.Observer and data-driven-model-based fault detection in power plant coal mills[J].IEEE Transactions on Energy Conversion,2008,23(2):659-668.
[33] ZHAO Chun-hui,WANG Fu-li,MAO Zhi-zhong,et al.Improved batch process monitoring and quality prediction based on multiphase statistical analysis[J].Industrial and Engineering Chemistry Research,2008,47(3):835-849.
[34] YU Jie,QIN S J.Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models[J].AIChE Journal,2008,54(7):1811-1829.
[35] KRUGER U,DIMITRIADIS G.Diagnosis of process faults in chemical systems using a local partial least squares approach[J].AIChE Journal,2008,54(10):2581-2596.
[36] GE Zhi-qiang,SONG Zhi-huan.Online monitoring of nonlinear multiple mode processes based on adaptive local model approach[J].Control Engineering Practice,2008,16(12):1427-1437.
[37] ZHOU Dong-hua H,LI Gang,QIN S J.Total projection to latent structures for process monitoring[J].AIChE Journal,2010,56(1):168-178.
[38] KAMP M,TANAKA S,HASEBE S,et al.Monitoring independent components for fault detection[J].AIChE Journal,2003,49(4):969-976.
[39] LEE G,HAN C H,YOON E S.Multiple-fault diagnosis of the tennessee eastman process based on system decomposition and dynamic PLS[J].Industrial and Engineering Chemistry Research,2004,43(25):8037-8048.
[40] ZHANG Y,QIN S J.Fault detection of nonlinear processes using multiway kernel independent component analysis[J].Industrial and Engineering Chemistry Research,2007,46(23):7780-7787.
[41] LEE J M,QIN S J,LEE I B.Fault detection and diagnosis based on modified independent component analysis[J].AIChE Journal,2006,52(10):3501-3514.
[42] 周东华,李钢,李元.数据驱动的工业过程故障诊断技术:基于PCA与PLS的方法[M].北京:科学出版社,2011.
[43] ZHANG S,GANESAN R.Multivariable trend analysis using neural networks for intelligent diagnostics of rotating machinery[J].Journal of Engineering for Gas Turbines and Power,1997,119(2):378-384.
[44] WANG Peng,VACHTSEVANOS G.Fault prognostics using dynamic wavelet neural networks[J].AI EDAM,2002,15(4):349-365.
[45] WANG W Q,GOLNARAGHI M F,ISMAIL F.Prognosis of machine health condition using neuro-fuzzy systems[J].Mechanical Systems and Signal Processing,2004,8(4):813-831.
[46] ZHANG Xiao-dong,XU R,KWAN C,et al.An integrated approach to bearing fault diagnostics and prognostics[C]//Proceedings of American Control Conference.Portland:IEEE Press,2005:2750-2755.
[47] DONG Ming,HE D.A segmental hidden semi-Markov model(HSMM)-based diagnostics and prognostics framework and methodology[J].Mechanical Systems and Signal Processing,2007,21(5):2248-2266.
[48] LI Gang,QIN S J,JI Yin-dong,et al.Reconstruction based fault prognosis for continuous processes[J].Control Engineering Practice,2010,18(1)):1211-1219.
[49] 马洁,徐小力,周东华.旋转机械的故障预测方法综述[J].自动化仪表,2011,32(8):1-3.

相似文献/References:

[1]冯远静,李良福,冯祖仁.粗糙集CMAC神经网络故障诊断策略[J].华侨大学学报(自然科学版),2004,25(3):318.[doi:10.3969/j.issn.1000-5013.2004.03.023]
　Feng Yuanjing,Li Liangfu,Feng Zuren.Rough Set-Based CMAC Neural Network for Fault Diagrosis[J].Journal of Huaqiao University(Natural Science),2004,25(6):318.[doi:10.3969/j.issn.1000-5013.2004.03.023]
[2]姚若苹,余尤好,陈培民,等.11相无刷励磁机故障工况的谐波分析[J].华侨大学学报(自然科学版),2007,28(2):127.[doi:10.3969/j.issn.1000-5013.2007.02.005]
　YAO Ruo-ping,YU You-hao,CHEN Pei-ming,et al.Harmonic Analysis of the Fault Cases in 11-Phase Brushless Exciter Based on FFT[J].Journal of Huaqiao University(Natural Science),2007,28(6):127.[doi:10.3969/j.issn.1000-5013.2007.02.005]
[3]刘少谦,黄宜坚.应用时间序列分析的液压溢流阀故障诊断法[J].华侨大学学报(自然科学版),2007,28(3):228.[doi:10.3969/j.issn.1000-5013.2007.03.002]
　LIU Shao-qian,HUANG Yi-jian.Fault Diagnosis of Hydraulic Relief Valve Using Time Series Analysis[J].Journal of Huaqiao University(Natural Science),2007,28(6):228.[doi:10.3969/j.issn.1000-5013.2007.03.002]
[4]蔡奇志,黄宜坚.三谱切片在调速阀故障诊断中的应用[J].华侨大学学报(自然科学版),2009,30(1):16.[doi:10.11830/ISSN.1000-5013.2009.01.0016]
　CAI Qi-zhi,HUANG Yi-jian.Fault Diagnosis of the Speed Control Valve Using the Slices of Trispectrum[J].Journal of Huaqiao University(Natural Science),2009,30(6):16.[doi:10.11830/ISSN.1000-5013.2009.01.0016]
[5]蒋雨燕,黄宜坚.调速阀故障诊断的AR双谱定阶方法比较[J].华侨大学学报(自然科学版),2009,30(2):123.[doi:10.11830/ISSN.1000-5013.2009.02.0123]
　JIANG Yu-yan,HUANG Yi-jian.Comparison of Different Methods of Determining the Order of Autoregressive Bi-Spectrum[J].Journal of Huaqiao University(Natural Science),2009,30(6):123.[doi:10.11830/ISSN.1000-5013.2009.02.0123]
[6]成琴,王启志.变压器油中溶解气体的智能诊断[J].华侨大学学报(自然科学版),2010,31(3):260.[doi:10.11830/ISSN.1000-5013.2010.03.0260]
　CHENG Qin,WANG Qi-zhi.Intelligent Diagnosis of Dissolved Gases in Transformer Oil[J].Journal of Huaqiao University(Natural Science),2010,31(6):260.[doi:10.11830/ISSN.1000-5013.2010.03.0260]
[7]王理停,黄宜坚.关联维数的超声波电机故障诊断[J].华侨大学学报(自然科学版),2011,32(4):368.[doi:10.11830/ISSN.1000-5013.2011.04.0368]
　WANG Li-ting,HUANG Yi-jian.Fault Diagnosis of Ultrasonic Motor Based on Correlation Dimension[J].Journal of Huaqiao University(Natural Science),2011,32(6):368.[doi:10.11830/ISSN.1000-5013.2011.04.0368]
[8]高宇,黄宜坚.采用关联维的溢流阀故障诊断[J].华侨大学学报(自然科学版),2012,33(3):241.[doi:10.11830/ISSN.1000-5013.2012.03.0241]
　GAO Yu,HUANG Yi-jian.Fault Diagnosis of Relief Valve by Using Correlation Dimension[J].Journal of Huaqiao University(Natural Science),2012,33(6):241.[doi:10.11830/ISSN.1000-5013.2012.03.0241]
[9]张妮,田学民,蔡连芳.基于改进动态等距离映射的非线性动态故障诊断方法[J].华侨大学学报(自然科学版),2012,33(6):621.[doi:10.11830/ISSN.1000-5013.2012.06.0621]
　ZHANG Ni,TIAN Xue-min,CAI Lian-fang.Nonlinear Dynamic Fault Diagnosis Method Based on Improved Isometric Mapping[J].Journal of Huaqiao University(Natural Science),2012,33(6):621.[doi:10.11830/ISSN.1000-5013.2012.06.0621]
[10]陈建灿,刘晓梅.AR双谱的电梯机械故障诊断[J].华侨大学学报(自然科学版),2013,34(3):258.[doi:10.11830/ISSN.1000-5013.2013.03.0258]
　CHEN Jian-can,LIU Xiao-mei.AR Bispectrum in Fault Diagnosis of Elevator Machinery[J].Journal of Huaqiao University(Natural Science),2013,34(6):258.[doi:10.11830/ISSN.1000-5013.2013.03.0258]

备注/Memo

备注/Memo:
收稿日期: 2012-06-15
通信作者: 马洁(1965-),女,副教授,主要从事数据驱动的动态系统故障预测的研究.E-mail:mjbeijing@163.com.
基金项目: 国家自然科学基金资助项目(61273173, 61028010, 61021063); 北京市自然科学基金资助项目( 4122029)
更新日期/Last Update: 2012-11-20