Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map

Abstract

A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern (SP) framework integrated with a self-organizing map (SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman (TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes. Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.

Keywords

statistic pattern framework / self-organizing map / fault diagnosis / process monitoring

References

[1] ChiangL H, BraatzR D, RussellE LFault detection and diagnosis in industrial systems [M], 2001, Berlin, Germany, Springer

[2] GeZ-q, SongZ-huanMultivariate statistical process control: Process monitoring methods and applications [M], 2013, Berlin, Germany, Springer

[3] ChiangL H, RussellE L, BraatzR D. Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis [J]. Chemometrics and intelligent laboratory systems, 2000, 50(2): 243-252

[4] JiangQ-c, YanX-feng. Statistical monitoring of chemical processes based on sensitive kernel principal components [J]. Chinese Journal of Chemical Engineering, 2013, 21(6): 633-643

[5] OdioweiP E P, CaoYi. Nonlinear dynamic process monitoring using canonical variate analysis and kernel density estimations [J]. IEEE Transactions on Industrial Informatics, 2010, 6(1): 36-45

[6] KanoM, HasebeS, HashimotoI, OhnoH. Statistical process monitoring based on dissimilarity of process data [J]. AIChE Journal, 2002, 48(6): 1231-1240

[7] YelamosI, EscuderoG, GraellsM, PuigjanerL. Performance assessment of a novel fault diagnosis system based on support vector machines [J]. Computers & Chemical Engineering, 2009, 33(1): 244-255

[8] YuJ, QinS J. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models [J]. AIChE Journal, 2008, 54(7): 1811-1829

[9] YuJ-bo. Hidden Markov models combining local and global information for nonlinear and multimodal process monitoring [J]. Journal of Process Control, 2010, 20(3): 344-359

[10] WangC-j, ZhaoY-hong. A new fault detection method based on artificial immune systems [J]. Asia-Pacific Journal of Chemical Engineering, 2008, 3(6): 706-711

[11] ZhaoJ-s, ShuY-d, ZhuJ-f, DaiY-yang. An online fault diagnosis strategy for full operating cycles of chemical processes [J]. Industrial & Engineering Chemistry Research, 2014, 53(13): 5015-5027

[12] HeQ P, WangJ. Statistics pattern analysis: A new process monitoring framework and its application to semiconductor batch processes [J]. AIChE Journal, 2011, 57(1): 107-121

[13] WangJ, HeQ P. Multivariate statistical process monitoring based on statistics pattern analysis [J]. Industrial & Engineering Chemistry Research, 2010, 49(17): 7858-7869

[14] GaliciaH J, HeQ P, WangJ. A comprehensive evaluation of statistics pattern analysis based process monitoring [C]. International Symposium on Advanced Control of Chemical Processes, Furama Riverfront, 2012, Singapore, ADCHEM: 39-44

[15] ChenX-y, YanX-feng. Fault diagnosis in chemical process based on self-organizing map integrated with fisher discriminant analysis [J]. Chinese Journal of Chemical Engineering, 2013, 21(4): 382-387

[16] WuS-t, ChowT W S. Induction machine fault detection using SOM-based RBF neural networks [J]. IEEE Transactions on Industrial Electronics, 2004, 51(1): 183-194

[17] SirolaM, TalonenJ, LampiG. SOM based methods in early fault detection of nuclear industry [C]. 17th European Symposium on Artificial Neural Networks, 2009, Burge, Belgium, ESANN

[18] ZhongF, ShiT-l, HeTao. Fault diagnosis of motor bearing using self-organizing maps [C]. Electrical Machines and Systems, 2005. ICEMS 2005. Proceedings of the Eighth International Conference on, 2005, Nanjing, IEEE: 2411-2414

[19] ChenX-y, YanX-feng. Using improved self-organizing map for fault diagnosis in chemical industry process [J]. Chemical Engineering Research and Design, 2012, 90(12): 2262-2277

[20] KohonenTSelf-organizing maps [M], 2001, Germany, Springer

[21] UltschAU*-matrix: A tool to visualize clusters in high dimensional data [M], 2003, Berlin, Fachbereich Mathematik und Informatik

[22] NgY S, SrinivasanR. Multivariate temporal data analysis using self-organizing maps. 2. Monitoring and diagnosis of multistate operations [J]. Industrial & Engineering Chemistry Research, 2008, 47(20): 7758-7771

[23] DowmsJ J, VogelE F. A plant-wide industrial process control problem [J]. Computers & Chemical Engineering, 1993, 17(3): 245-255

[24] LymanP R, GeorgakisC. Plant-wide control of the Tennessee Eastman problem [J]. Computers & Chemical Engineering, 1995, 19(3): 321-331

[25] ZhuZ-b, SongZ-h, PalazogluA. Transition process modeling and monitoring based on dynamic ensemble clustering and multiclass support vector data description [J]. Industrial & Engineering Chemistry Research, 2011, 50(24): 13969-13983