Skip to main content
SpringerLink
Log in

Nonlinear feature selection using Gaussian kernel SVM-RFE for fault diagnosis

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Feature selection can directly ascertain causes of faults by selecting useful features for fault diagnosis, which can simplify the procedures of fault diagnosis. As an efficient feature selection method, the linear kernel support vector machine recursive feature elimination (SVM-RFE) has been successfully applied to fault diagnosis. However, fault diagnosis is not a linear issue. Thus, this paper introduces the Gaussian kernel SVM-RFE to extract nonlinear features for fault diagnosis. The key issue is the selection of the kernel parameter for the Gaussian kernel SVM-RFE. We introduce three classical and simple kernel parameter selection methods and compare them in experiments. The proposed fault diagnosis framework combines the Gaussian kernel SVM-RFE and the SVM classifier, which can improve the performance of fault diagnosis. Experimental results on the Tennessee Eastman process indicate that the proposed framework for fault diagnosis is an advanced technique.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6(1):37–66

    Google Scholar 

  2. Chiang LH, Russell EL, Braatz RD (2000) Fault diagnosis in chemical processes using fisher discriminant analysis, discriminant partial least squares, and principal component analysis. Chemometr Intell Lab Syst 50 (2):243–252

    Article  Google Scholar 

  3. Cho JH, Lee JM, Choi SW, Lee D, Lee IB (2005) Fault identification for process monitoring using kernel principal component analysis. Chem Eng Sci 60(1):279–288

    Article  Google Scholar 

  4. Downs JJ, Vogel EF (1993) A plant-wide industrial process control problem. Comput Chem Eng 17 (3):245–255

    Article  Google Scholar 

  5. Duda RO, Hart PE (1973) Pattern recognition and scene analysis. Wiley, New York

    MATH  Google Scholar 

  6. Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1):389–422

    Article  MATH  Google Scholar 

  7. Hotelling H (1947) Multivariate quality control. Techn Stat Anal 31(3):17–20

    Google Scholar 

  8. Hsu CC, Chen MC, Chen LS (2010) Intelligent ICA-SVM fault detection fault detector for non-gaussian multivariate process monitoring. Expert Syst Appl 37(4):3264–3273

    Article  Google Scholar 

  9. Jackson JE (1959) Quality control methods for several related variables. Technometrics 1(4):359–377

    Article  MathSciNet  Google Scholar 

  10. Jiang QC, Yan XF, Huang B (2015) Performance-driven distributed PCA process monitoring based on fault-relevant variable selection and bayesian inference. IEEE Trans Ind Electron 63(1):377–386

    Article  Google Scholar 

  11. Kleer JD, Kurien J (2004) Fundamentals of model-based diagnosis. In: 5th IFAC symposium on fault detection, supervision, and safety of technical processes(safeprocess)

  12. Kramer MA, Palowitch BL (1987) A rule-based approach to fault diagnosis using the signed directed graph. Aiche J 33(7):1067–1078

    Article  MathSciNet  Google Scholar 

  13. Lee JM, Yoo CK, Lee IB (2004) Statistical process monitoring with independent components analysis. J Process Control 14(5):467–485

    Article  Google Scholar 

  14. Mahadevan S, Shah SL (2009) Fault detection and diagnosis in process data using one-class support vector machines. J Process Control 19(10):1627–1639

    Article  Google Scholar 

  15. Mao Y, Zhou XB, Pi DY, Sun YX, Wong STC (2005) Parameters selection in gene selection using gaussian kernel support vector machines by genetic algorithm. J Zhejiang Univ (Sci) 6(10):961–73

    Article  Google Scholar 

  16. Mao Y, Zhou XB, Yin Z, Pi DY, Sun YX, Wong STC (2006) Gene selection using gaussian kernel support vector machine based recursive feature elimination with adaptive kernel width strategy. In: Proceedings of first international conference on rough sets and knowledge technology, lecture notes in computer science, vol 4062, pp 799–806

  17. Mesleh AMA (2007) Chi square feature extraction based SVMs arabic language text categorization system. J Comput Sci 3(6):430–435

    Article  Google Scholar 

  18. Pearson K (1900) On lines and planes of closest fit to points in space. Philos Mag Ser B 2(11):559–572

    Article  MATH  Google Scholar 

  19. Qin SJ (2012) Survey on data-driven industrial process monitoring and diagnosis. Annu Rev Control 36 (2):220–234

    Article  Google Scholar 

  20. Rubio G, Pomares H, Rojas I, Herrera LJ (2011) A heuristic method for parameter selection in LS-SVM: application to time series prediction. Int J Forecast 27(3):725–739

    Article  Google Scholar 

  21. Shieh MD, Yang CC (2008) Multiclass SVM-RFE for product form feature selection. Expert Syst Appl 35 (1-2):531–541

    Article  Google Scholar 

  22. Vapnik VN (1995) The natural of statistical learning theory. Springer, New York

    Book  MATH  Google Scholar 

  23. Vapnik VN (2010) Statistical learning theory. Enc Sci Learn 41(4):3185–3185

    Google Scholar 

  24. Venkatasubramanian V, Rengaswamy R, Yin K, Kavuri SN (2003) A review of process fault detection and diagnosis: part III: process history based methods. Comput Chem Eng 27(3):327–346

    Article  Google Scholar 

  25. Venkatasubramanian V, Rengaswamy R, Yin K, Kavuri SN (2003) A review of process fault detection and diagnosis: part I: quantitative model-based methods. Comput Chem Eng 27(3):293–311

    Article  Google Scholar 

  26. Wang H, Song ZH, Wang H (2002) Statistical process monitoring using improved PCA with optimized sensor location. J Process Control 12(6):735–744

    Article  Google Scholar 

  27. Wu J, Wang J, Liu L (2007) Feature extraction via KPCA for classification of gait patterns. Hum Mov Sci 26(3):393–411

    Article  Google Scholar 

  28. Wu YC, Lee Y, Yang JC (2008) Robust and efficient multiclass SVM models for phrase pattern recognition. Pattern Recogn 41(9):2874–2889

    Article  MATH  Google Scholar 

  29. Xu Y, Zhang D, Song F, Yang JY, Jing Z, Li M (2007) A method for speeding up feature extraction based on KPCA. Neurocopmuting 70(4-6):1056–1061

    Article  Google Scholar 

  30. Xu ZB, Dai MW, Meng SY (2009) Fast and efficient strateqies for model selection of gaussian support vector machine. IEEE Trans Syst Man Cybern B Cybern 39(5):1292–1307

    Article  Google Scholar 

  31. Yin S, Ding S, Haghani A, Hao H, Zhang P (2012) A comparison study of basis data-driven fault diagnosis and process monitoring methods on the benchmark tennessee eastman process. J Process Control 22:1567–1581

    Article  Google Scholar 

  32. Zhang K, Qian K, Chai Y, Liu J (2014) Research on fault diagnosis of tennessee eastman process based on KPCA and SVM. In: 2014 Seventh international symposium on computational intelligence and design, vol 1, pp 490–495

  33. Zhang L, Zhou WD, Li FZ (2012) Kernel sparse representation-based classifier ensemble for face recognition. IEEE Trans Signal Process 60(4):1684–1695

    Article  MathSciNet  MATH  Google Scholar 

  34. Zhou H, Hastie T (2005) Regularization and variable selection via elastic net. J R Stat Soc Ser B 67 (2):301–320

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61373093, by the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20140008, by the Soochow Scholar Project, by the Six Talent Peak Project of Jiangsu Province of China, and by the Collaborative Innovation Center of Novel Software Technology and Industrialization.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Soochow University, Suzhou, 215006, Jiangsu, China

    Yangtao Xue, Li Zhang, Bangjun Wang, Zhao Zhang & Fanzhang Li

  2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, 210023, Jiangsu, China

    Li Zhang & Fanzhang Li

Authors
  1. Yangtao Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bangjun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fanzhang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xue, Y., Zhang, L., Wang, B. et al. Nonlinear feature selection using Gaussian kernel SVM-RFE for fault diagnosis. Appl Intell 48, 3306–3331 (2018). https://doi.org/10.1007/s10489-018-1140-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-018-1140-3

Keywords

Search

Navigation