基于数据驱动的故障诊断技术研究现状及展望

doi:10.11896/j.issn.1002-137X.2017.6A.008

摘要/Abstract

摘要： 对基于数据驱动的过程故障诊断方法进行了总结和划分,其中包含多元统计方法、机器学习方法、流形学习方法等。将各类基于数据驱动的故障诊断方法的原理、研究进展及其在工业过程中的应用进行了描述和分析,最后指出这一领域中需要进一步解决的问题以及近期的研究热点。

关键词: 数据驱动方法,故障诊断,多元统计方法

Abstract: Fault diagnosis methods based on data-driven were summarized and divided,which include multivariate statistical methods,machine learning,manifold learning and so on.The principle,research progress and different methods application were analyzed and described.The problems needed to solve and recent research hotspots were addressed finally.

Key words: Data-driven method,Fault diagnosis,Multivariate statistical methods

张妮,车立志,吴小进. 基于数据驱动的故障诊断技术研究现状及展望[J]. 计算机科学, 2017, 44(Z6): 37-42. https://doi.org/10.11896/j.issn.1002-137X.2017.6A.008

ZHANG Ni, CHE Li-zhi and WU Xiao-jin. Present Situation and Prospect of Data-driven Based Fault Diagnosis Technique[J]. Computer Science, 2017, 44(Z6): 37-42. https://doi.org/10.11896/j.issn.1002-137X.2017.6A.008

参考文献

[1] VENKATASUBRAMANIAN V,RENGASWAMY R,KAVU-RI SURYAN,et al.A Review of Process Fault Detection and Diagnosis Part III:Process History based Methods[J].Compu-ters and Chemical Engineering,2003,27(3):327-346.
[2] 刘强,柴天佑,秦泗钊,等.基于数据和知识的工业过程监视及故障诊断综述[J].控制与决策,2010,25(6):801-807.
[3] ALCALA CARLOS F,JOE Q S.Analysis and Generalization of Fault Diagnosis Methods for Process Monitoring[J].Journal of Process Control,2011,21(3):322-330.
[4] 文成林,吕菲亚,包哲静,等.基于数据驱动的微小故障诊断方法综述[J].自动化学报,2016,42(9):1285-1299.
[5] HASTIE T,STUETZLE W.Principal Curves[J].Journal of the American Statistic Association,1989,84(4):502-516.
[6] KRAMER MARK A.Nonlinear Principal Component Analysis using Auto-associative Neural Networks[J].Aiche Journal,1991,37(2):233-243.
[7] 邓晓刚,田学民.一种基于KPCA的非线性故障诊断方法[J].山东大学学报(工学版),2005,35(3):103-106.
[8] XU Y,LIU Y,ZHU Q X.Multivariate Time Delay Analysisbased Local KPCA Fault Prognosis Approach for Nonlinear Processes[J].Chinese Journal of Chemical Engineering,2016,24(10):1413-1422.
[9] YAN A M,KERSCHEN G,DE BOE P,et al.Structural Damage Diagnosis Under Changing Environmental Conditions Part II:Local PCA for Nonlinear Analysis[J].Mechnical System Signal Process,2005,19(4):865-880.
[10] YU H Y,KHAN F,GARANIYA V.Nonlinear Gaussian Belief Network based Fault Diagnosis for Industrial Processes[J].Journal of Process Control,2015,35(c):178-120.
[11] YU H Y,KHAN F,GARANIYA V.A Sparse PCA for Nonli-near Fault Diagnosis and Robust Feature Discovery of Industrial Processes[J].AICHE Journal,2016,62(5):1494-1513.
[12] KU W F,STORER R,GEORGAKIS C.Disturbance Detectionand Isolation by Dynamic Principal Component Analysis[J].Che-mometrics and Intelligent Laboratory Systems,1995,30(1):179-196.
[13] HUANG J,YAN X F.Dynamic Process Fault Detection and Dia-gnosis based on Dynamic Principal Component Analysis,Dynamic Independent Component Analysis and Bayesian inference[J].Chemometrics and Intelligent Laboratory Systems,2015,148:115-127.
[14] LIU X Q,KRUGER U.Moving Window Kernel PCA for Adaptive Monitoring of Nonlinear Processes[J].Chemometrics and Intelligent Laboratory Systems,2009,96(2):132-143.
[15] KHEDIRI ISSAM B,LIMAM M,WEIHS C.Variable Window Adaptive Kernel Principal Component Analysis for Nonlinear Nonstationary Process Monitoring[J].Computers & Industrial Engineering,2011,61(3):437-446.
[16] JENG J C.Adaptive Process Monitoring using Efficient Recursive PCA and Moving Window PCA Algorithms[J].Journal of the Taiwan Institute of Chemical Engineers,2010,41(4):475-481.
[17] CHENG C Y,HSU C C,CHEN M C.Adaptive Kernel Principal Component Analysis (KPCA) for Monitoring Small Disturbances of Nonlinear Processes[J].Industrial & Engineering Chemistry Research,2010,49(5):2254-2262.
[18] LIU K L,FEI Z S,YUE B X,et al.Adaptive Sparse Principal Component Analysis for Enhanced Process Monitoring and Fault Isolation[J].Chemometrics and Intelligent Laboratory Systems,2015,146:426-436.
[19] 谭帅,常玉清,王福利,等.基于GMM的多模态过程模态识别与过程监测[J].控制与决策,2015,30(1):53-58.
[20] JIANG Q C,HUANG B,YAN X F.GMM and Optimal Principal Components-based Bayesian Method for Multimode Fault Diagnosis[J].Computers & Chemical Engineering,2016,84:338-349.
[21] YANG Y W,MA Y X,SONG B,et al.An Aligned MixtureProbabilistic Principal Component Analysis for Fault Detection of Multimode Che-mical Processes[J].Chinese Journal of Che-mical Engineering,2015,23(8):1357-1363.
[22] GE Z Q,SONG Z H.Two-level Multiblock Statistical Monitoring for Plant-wide Processes[J].Korean Journal of Chemical Engineering,2009,26(6):1467-1475.
[23] LIU K L,JIN X,FEI Z S,et al.Adaptive Partitioning PCAModel for Improving Fault Detection and Isolation[J].Chinese Journal of Chemical Engineering,2015,23(6):981-991.
[24] WANG G Z,LIU J C,LI Y,et al.Fault Diagnosis of Chemical Processes based on Partitioning PCA and Variable Reasoning Strategy[J].Chinese Journal of Chemical Engineering,2016,2016,24(7):869-880.
[25] YIN S,DING S X,ABANDAN S A,et al.Data-driven Monitoring for Stochastic Systems and Its Application on Batch Process[J].International Journal of Systems Science,2013,44(7):1366-1376.
[26] YANG Y H,LI X L,LIU X Z,et al.Wavelet Kernel Entropy Component Analysis with Application to Industrial Process Monitoring[J].Neurocomputing,2015,147(1):395-402.
[27] HUANG J,YAN X F.Related and Independent Variable Fault Detection based on KPCA and SVDD[J].Journal of Process Control,2016,39:88-99.
[28] ZHAO C H,GAO F R.Fault Subspace Selection ApproachCombined With Analysis of Relative Changes for Reconstruction Modeling and Multifault Diagnosis[J].IEEE Transactions on Control Systems Technology,2015,24(3):928-939.
[29] 李晗,萧德云.基于数据驱动的故障诊断方法综述[J].控制与决策,2011,26(1):1-16.
[30] LE C,CHOI S W,LEE I B.Variable Reconstruction and Sensor Fault Identification using Canonical Variate Analysis[J].Journal of Process Control,2006,16(7):747-761.
[31] 邓晓刚,田学民.基于鲁棒规范变量分析的故障诊断方法[J].控制与决策,2008,23(4):415-419.
[32] YAO Y,GAO F R.Subspace Identification for Two-dimensional Dynamic Batch Process Statistical Monitoring[J].Chemical Engineering Science,2008,63(13):3411-3418.
[33] 陈国金,梁军,钱积新.独立元分析方法(ICA)及其在化工过程监控和故障诊断中的应用[J].化工学报,2003,54(10):1474-1477.
[34] YOO C K,LEE J M,VANROLLEGHEM P A,et al.On-lineMonitoring of Batch Processes using Multiway Independent Component Analysis[J].Chemometrics and Intelligent Laboratory Systems,2004,71(2):151-163.
[35] LEE J,KANG B,KANG S H.Integrating Independent Component Analysis and Local Outlier Factor for Plant-wide Process Monitoring[J].Journal of Process Control,2011,21(7):1011-1021.
[36] GEORGE S,BEN H A.Dynamic Independent Component Ana-lysis Approach for Fault Detection and Diagnosis[J].Expert Systems with Applications,2010,37(12):8606-8617.
[37] FAN J C,WANG Y Q.Fault Detection and Diagnosis of Non-linear Non-Gaussian Dynamic Processes using Kernel Dynamic Independent Component Analysis[J].Information Sciences,2014,259(3):369-379.
[38] LUO L,SU H Y,BAN L.Independent Component Analysis-based Sparse Autoencoder in the Application of Fault Diagnosis[J].Intelligent Control & Automation,2015,52(1):1378-1382.
[39] RAD M A A,YAZDAN PANAH M J.Designing SupervisedLocal Neural Network Classifiers based on EM Clustering for Fault Diagnosis of Tennessee Eastman Process[J].Chemome-trics and Intelligent Laboratory Systems,2015,146:149-157.
[40] XU Y,DENG X G.Fault Detection of Multimode Non-Gaussian Dynamic Process using Dynamic Bayesian Independent Component Analysis[J].Neurocomputing,2016,200(c):70-79.
[41] CHEN X Y,YAN X F.Fault Diagnosis in Chemical Processbased on Self-organizing Map Integrated with Fisher Discriminant Analysis[J].Chinese Journal of Chemical Engineering,2013,21(4):382-387.
[42] GE Z Q,ZHONG S Y,ZHANG Y W.Semi-supervised Kernel Learning for FDA Model and Its Application for Fault Classification in Industrial Processes[J].IEEE Transactions on Industrial Informatics,2016,12(4):1551-3203.
[43] FENG J,WANG J,ZHANG H G,et al.Fault Diagnosis Method of Joint Fisher Discriminant Analysis based on the Local and Global Manifold Learning and Its Kernel Version[J].IEEE Transactions on Automation Science and Engineering,2016,13(1):122-133.
[44] KRESTA K J V,MACGREGOR J F,MARLIN M T E.Multivariate Statistical Monitoring of Process Operating Performance[J].Canadian Journal of Chemical Engineering,1991,69(1):35-47.
[45] TIINA K,MAURI S,SIRKKA-LIISA J J.An Online Application of Dynamic PLS to a Dearomatization Process[J].Compu-ters and Chemical Engineering,2004,28(12):2611-2619.
[46] ZHAO S J,ZHANG J,XU Y M.Performance Monitoring ofProcesses with Multiple Operating Modes Through Multiple PLS Models[J].Journal of Process Control,2006,16(7):763-772.
[47] LEE H W,LEE M W,JONG M P.Multi-scale Extension of PLS Algorithm for Advanced on-line Process Monitoring[J].Chemometrics and Intelligent Laboratory Systems,2009,98(2):201-212.
[48] ZHAO C H,MO S Y,GAO F R.Statistical Analysis and Online Monitoring for Handling Multiphase Batch Processes with Varying Durations[J].Journal of Process Control,2011,21(6):817-829.
[49] YU J.A Nonlinear Kernel Gaussian Mixture Model based Infe-rential Monitoring Approach for Fault Detection and Diagnosis of Chemical Processes[J].Chemical Engineering Science,2012,68(1):506-519.
[50] YIN S,WANG G,YANG X.Robust PLS Approach for KPI-related Prediction and Diagnosis against Outliers and Missing Data[J].International Journal of Systems Science,2014,45(7):1375-1382.
[51] YIN S,ZHU X P,KAYNAK O.Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis[J].IEEE Tran-sactions on Industrial Electronics,2015,62(3):1651-1658.
[52] JIA Q L,ZHANG Y W.Quality-related Fault Detection Ap-proach based on Dynamic Kernel Partial Least Squares[J].Chemical Engineering Research and Design,2016,106:242-252.
[53] ZHANG Y W,FA Y P,YANG N.Fault Diagnosis of Multimode Processes based on Similarities[J].IEEE Transactions on Industrial Electronics,2016,63(4):2606-2614.
[54] SYLVAIN V,TEODOR T,ABDESSAMAD K.Fault Diagnosis of Industrial Systems by Conditional Gaussian Network Including a Distance Rejection Criterion[J].Engineering Applications of Artificial Intelligence,2010,23(7):1229-1235.
[55] BARAKAT M,DURAUX F,LEFEBVRE D,et al.Self Adap-tive Growing Neural Network Classifier for Faults Detection and Diagnosis[J].Neurocomputing,2011,74(18):3865-3876.
[56] YU H Y,KHAN F,GARANIYA V,et al.Self-Organizing Map based Fault Diagnosis Technique for Non-Gaussian Processes[J].Industrial & Engineering Chemistry Research,2014,53(21):8831-8843.
[57] GE Z Q,CHEN X R.Supervised Linear Dynamic System Model for Quality Related Fault Detection in Dynamic Processes[J].Journal of Process Control,2016,44:224-235.
[58] VAPNIK V,GOLOWICH S,SMOLA A.Support Vector Me-thod for Function Approximation,Regression Estimation,and Signal Processing[J].Advances in Neural Information Proces-sing Systems,1996,9:281-287.
[59] 马笑潇,黄席樾,柴毅.基于SVM的二叉树多类分类算法及其在故障诊断中的应用[J].控制与决策,2003,18(3):272-284.
[60] WU Q,LAW R.Complex System Fault Diagnosis based on aFuzzy Robust Wavelet Support Vector Classifier and an Adaptive Gaussian Particle Swarm Optimization[J].Information Sciences,2010,23(1):4514-4528.
[61] GE Z Q,GAO F R,SONG Z H.Batch Process Monitoring based on Support Vector Data Description Method[J].Journal of Process Control,2011,21(6):949-959.
[62] KHEDIRI I B,WEIHS C,LIMAM M.Kernel k-means Cluste-ring based Local Support Vector Domain Description Fault Detection of Multimodal Processes[J].Expert Systems with Applications,2012,39(2):2166-2171.
[63] LIU Z W,CAO H R,CHEN X F,et al.Multi-fault Classification based on Wavelet SVM with PSO Algorithm to Analyze Vibration Signals from Rolling Element Bearings[J].Neurocompu-ting,2013,99(1):399-410.
[64] WANG A N,SHA M,LIU L M,et al.A New Process Industry Fault Diagnosis Algorithm based on Ensemble Improved Binary-Tree SVM[J].Chinese Journal of Electronics,2015,24(2):258-262.
[65] YIN Z Y,HOU J N.Recent Advances on SVM based Fault Diagnosis and Process Monitoring in Complicated Industrial Processes[J].Neurocomputing,2016,174(PB):643-650.
[66] DENG X G,TIAN X M,CHEN S.Modified Kernel Principal Component Analysis based on Local Structure Analysis and Its Application to Nonlinear Process Fault Diagnosis[J].Chemometrics and Intelligent Laboratory Systems,2013,127(16):195-209.
[67] MIAO A M,GE Z Q,SONG Z H,et al.Nonlocal Structure Constrained Neighborhood Preserving Embedding Model and Its Application for Fault Detection[J].Chemometrics and Intelligent Laboratory Systems,2015,142:184-196.
[68] 王健,冯健,韩志艳.基于流形学习的局部保持PCA算法在故障检测中的应用[J].控制与决策,2013,28(5):683-687.
[69] XIANG X Q,ZHOU J Z,LI C S,et al.Fault Diagnosis based on Walsh Transform and Rough Sets[J].Electric Power Systems Research,2009,23(4):1314-1326.
[70] ZHANG X Y,ZHOU J Z,GUO J,et al.Vibrant Fault Diagnosis for Hydroelectric Generator Units with a New Combination of Rough Sets and Support Vector Machine[J].Expert Systems with Applications,2012,39(3):2621-2628.
[71] 叶昊,王桂增,方崇智.小波变换在故障检测中的应用[J].自动化学报,1997,23(6):736-741.
[72] LEI Y G,LIN J,HE Z J,et al.A Review on Empirical Mode Decomposition in Fault Diagnosis of Rotating Machinery [J].Mechanical Systems and Signal Processing,2013,35(1/2):108-126.
[73] TIAN Y,MA J,LU C,et al.Rolling Bearing Fault Diagnosis Under Variable Conditions Using LMD-SVD and Extreme Learning Machine[J].Mechanism and Machine Theory,2015,90:175-186.
[74] YAN R Q,GAO R X,CHEN X F.Wavelets for Fault Diagnosis of Rotary Machines:A Review with Applications[J].Signal Processing,2014,96(5):1-15.
[75] 王耀南,李树涛.多传感器信息融合及其应用综述[J].控制与决策,2001,16(5):518-522.
[76] 朱大奇,刘永安.故障诊断的信息融合方法[J].控制与决策,2007,22(12):1321-1328.
[77] ZHANG F Y,GE Z Q.Decision Fusion Systems for Fault Detection and Identification in Industrial Processes[J].Journal of Process Control,2015,31:45-54.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed