Skip to main content
SpringerLink
Log in

Weighted and constrained possibilistic C-means clustering for online fault detection and isolation

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

In this paper, a new weighted and constrained possibilistic C-means clustering algorithm is proposed for process fault detection and diagnosis (FDI) in offline and online modes for both already known and novel faults. A possibilistic clustering based approach is utilized here to address some of the deficiencies of the fuzzy C-means (FCM) algorithm leading to more consistent results in the context of the FDI tasks by relaxing the probabilistic condition in FCM cost function. The proposed algorithm clusters the historical data set into C different dense regions without having precise knowledge about the number of the faults in the data set. The algorithm incorporates simultaneously possibilistic algorithm and local attribute weighting for time-series segmentation. This allows different weights to be allocated to different features responsible for the distinguished process faults which is an essential characteristic of proper FDI operations. A set of comparative studies have been carried out on the large-scale Tennessee Eastman industrial challenge problem and the DAMADICS actuator benchmark to demonstrate the superiority of the proposed algorithm in process FDI applications with respect to some available alternative approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Chiang LH, Russell EL, Braatz RD (2001) Fault detection and diagnosis in industrial systems. Springer, London

    Book  MATH  Google Scholar 

  2. Dunia R, Qin SJ, Edgar TF, McAvoy TJ (1996) Identification of faulty sensors using principal component analysis. AIChE J 42:2797–2812

    Article  Google Scholar 

  3. Lee JM, Qin SJ, Lee IB (2006) Fault detection and diagnosis based on modified independent component analysis. AIChE J 52(10):3501–3514

    Article  Google Scholar 

  4. Chiang LH, Russell EL, Braatz RD (2000) Fault diagnosis in chemical processes using Fisher discriminant analysis, partial least squares, and principal component analysis. Chemom Intell Lab Syst 50:243–492

    Article  Google Scholar 

  5. Wang X, Kruger U, Lennox B (2003) Recursive partial least squares algorithms for monitoring complex industrial processes. Control Eng Pract 11(6):613–632

    Article  Google Scholar 

  6. Raich AC, Cinar A (1996) Statistical process monitoring and disturbance diagnosis in multivariable continuous processes. AIChE J 42:995–1009

    Article  Google Scholar 

  7. Lieftucht D, Kruger U, Irwin GW (2006) Improved reliability in diagnosing faults using multivariate statistics. Comput Chem Eng 30(5):901–912

    Article  Google Scholar 

  8. Krishnapuram R, Keller JM (1993) A possibilistic approach to clustering. IEEE Trans Fuzzy Syst 1(2):98–110

    Article  Google Scholar 

  9. Widodo A, Yang BS (2007) Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Fault Diagn 21:2560–2574

    Google Scholar 

  10. Palade V, Patton RJ, Uppal FJ, Quevedo J, Daley S (2002) Fault diagnosis of an industrial gas turbine using neuro-fuzzy methods. In: IFAC, 2002

  11. Detroja KP, Gudi RD, Patwardhan SC (2006) A possibilistic clustering approach to novel fault detection and isolation. J Process Control 16:1055–1073

    Article  Google Scholar 

  12. Vasko KT, Toivonen HTTT (2002) Estimating the number of segments in time series data using permutation tests. IEEE Int Conf Data Mining 466–473

  13. Abonyi J, Feil B, Nemeth S, Arva P (2005) Modified Gath–Geva clustering for fuzzy segmentation of multivariate time-series. Fuzzy Sets Syst 140:39–56

    Article  MathSciNet  Google Scholar 

  14. Berkhin P (2002) Survey of clustering data mining techniques. Accrue Software, Inc., Fremont

  15. Berry M, Dumais S, Landaure T, Obrien G (1995) Using linear algebra for intelligent information retrieval. SIAM Rev 37:573–595

    Article  MathSciNet  MATH  Google Scholar 

  16. Mardina K, Kent J, Bibby J (1980) Multivariate analysis. Academic Press, San Diego

    Google Scholar 

  17. Law MHC, Figueiredo MAT, Jain AK (2004) Simultaneous feature selection and clustering using mixture models. IEEE Trans Pattern Anal Mach Intell 26:1154–1166

    Article  Google Scholar 

  18. Wettschereck D, Aha DW, Mohri T (1997) A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artif Intell 11:273–314

    Google Scholar 

  19. Domingos P (1997) Context sensitive feature selection for lazy learners. Artif Intell 11:227–253

    Article  Google Scholar 

  20. Srinivasan R, Qian MS (2007) State-specific key variables for monitoring multi-state processes. Chem Eng Res Des 85:1630–1644

    Google Scholar 

  21. He QP, Wang J, Qin SJ (2005) A new fault diagnosis method using fault directions in Fisher discriminant analysis. AIChE J 51(2):555–571

    Article  Google Scholar 

  22. Teppola P, Minkkinen P (1999) Possibilistic and fuzzy C-means clustering for process monitoring in an activated sludge waste-water treatment plant. J Chemom 13:445–459

    Article  Google Scholar 

  23. Frigui H, Nasraoui O (2004) Unsupervised learning of prototypes and attribute weights. Pattern Recogn 37:567–581

    Article  Google Scholar 

  24. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York

    MATH  Google Scholar 

  25. Downs JJ, Vogel EF (1993) A plant-wide industrial-process control problem. Comput Chem Eng 17:245–255

    Article  Google Scholar 

  26. Lin W, Qian Y, Li X (2000) Nonlinear dynamic principal component analysis for on-line process monitoring and diagnosis. Comput Chem Eng 24:423–429

    Article  Google Scholar 

  27. Ku W, Storer RH, Georgakis Ch (1995) Disturbance detection and isolation by dynamic principal component analysis. Chemom Intell Lab Syst 30:179–196

    Article  Google Scholar 

  28. Kelly PM (1994) An algorithm for merging hyperellipsoidal clusters. Technical Report LA-UR-94-3306, Los Alamos National Laboratory, Los Alamos, NM

  29. Syfert M, Patton R, Bartys M, Quevedo J (2003) Development and application of methods for actuator diagnosis in industrial control systems (Damadics): a benchmark study. In: Proceedings of the IFAC symposium safe process, pp 939–950

  30. Krishnapuram R, Keller JM (1996) A possibilistic C-means algorithm: insights and recommendations. IEEE Trans Fuzzy Syst 4(3):385–393

    Article  Google Scholar 

  31. Xie XL, Beni GA (1991) Validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 3(8):841–846

    Article  Google Scholar 

  32. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323

    Article  Google Scholar 

  33. Chen J, Howell J (2002) Towards distributed diagnosis of the Tennessee Eastman process benchmark. Control Eng Pract 10:971–987

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Control and Intelligence Processing Center of Excellence, School of ECE, University of Tehran, Tehran, Iran

    Soheil Bahrampour & Behzad Moshiri

  2. Department of Instrumentation and Automation, Petroleum University of Technology, Tehran, Iran

    Karim Salahshoor

Authors
  1. Soheil Bahrampour
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Behzad Moshiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Karim Salahshoor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soheil Bahrampour.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bahrampour, S., Moshiri, B. & Salahshoor, K. Weighted and constrained possibilistic C-means clustering for online fault detection and isolation. Appl Intell 35, 269–284 (2011). https://doi.org/10.1007/s10489-010-0219-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-010-0219-2

Keywords

Search

Navigation