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Multi-variable process data compression and defect isolation using wavelet PCA and genetic algorithm

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Abstract

This paper characterizes an approach to data compression and defect isolation for the multi-variable process by introducing the wavelet principal component analysis (PCA) and the genetic algorithms. In the defect analysis process, data compression is a critical phase. In our case of study, this can be achieved through the wavelet PCA. We can, through the use of such a technique, filter the data noise and the measurement errors by combining the PCA and the wavelet analysis. The genetic algorithm optimization feature is then employed in structuring the residues of the defect isolation. We present the principle of the PCA as well as the wavelet compression property. Then, we describe both the defect isolation classical method by structuring the residues and the principle of optimizing a problem by the genetic algorithms. The proposed approach is implemented on a statistic system and the Tennessee Eastman process. In comparison with other previous methods, the obtained results mirror the performance of the approach to defect isolation.

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References

  1. Chouaib C (2015) Mohamed Faouzi HARKAT and Messaoud DJEGHABA, “new adaptive kernel principal component analysis for nonlinear dynamic process monitoring”. Appl Math Inf Sci 9(4):1833–1845

    Google Scholar 

  2. Muzhir Shaban A-A, AlaaSulaiman A-W (2011) Multi-view face detection based on kernel principal component analysis and kernel support vector techniques. Int J Soft Comput ( IJSC ) Vol.2(2):1–13

    Article  Google Scholar 

  3. Ramahaleomiarantsoa (2012) &al, “Performances of the PCA method in electrical machines diagnosis using Matlab”, INTECH (chapter 4), pp.69–87

  4. Xueqin L et al (2011) Application of nonlinear PCA for fault detection in polymer extrusion processes. Sch Electron, Electr Eng Comput Sci vol20(issue 6):1141–1148

    Google Scholar 

  5. Yun Xu, Royston, Goodacre (2012), “Multiblock principal component analysis: an efficient tool for analyzing metabolomics data which contain two influential factors”, METABOLOMICS (Springer), Volume 8, Supplement 1, pp 37–51

  6. Molloy M, Martin EB (December 2013) Application of multiway principal component analysis for identification of process improvements in pharmaceutical manufacture. 10th IFAC International Symposium on Dynamics and Control of Process Systems, Mumbai, pp. 283–288

    Google Scholar 

  7. Zhikun H et al (2014) Adaptive PCA based fault diagnosis scheme in imperial smelting process. Isa Transaction, ICCA 2013 53(5):1446–1455

    Google Scholar 

  8. Shengkun X, Sridhar K (2013) Dynamic principal component analysis with Nonoverlapping moving window and its applications to epileptic EEG classification. Sci World J, Hindawi 2014:1–10

    Google Scholar 

  9. Zhikun H et al (2012) A simplified recursive dynamic PCA based monitoring scheme for imperial smelting process. Int J Innov Comput, Inf Control 8(4):2551–2561

    Google Scholar 

  10. Sharma LN et al (2012) Multichannel ECG data compression based on multiscale principal component analysis. IEEE Trans Inf Technol Biomed 16(4):730–736

    Article  Google Scholar 

  11. Padmavathi G Shanmugapriya D and Kalaivani M “Neural network approaches and MSPCA in vehicle acoustic signal classification using wireless sensor networks”, ICCCCT-2010, pp:372–376

  12. Jun Ding & et al (2011) “A method for urban traffic data compression based on wavelet -PCA”, Fourth International Joint Conference on Computational Sciences and Optimization, pp: 1030–1034

  13. Shengkun Xie and Sridhar Krishnan (2011) “Signal decomposition by multi-scale PCA and its applications to long-term EEG signal classification”, International Conference on Complex Medical Engineering, Harbin, China, pp: 532–537

  14. Vijaykumar Shinde D, Patil C. G and Sachin Ruikar D (2016) Wavelet based multi-scale principal component analysis for speech enhancement, Int J Eng Trends Technol, 2012, Volume3, Issue3, pp: 397–400.

  15. Lau CK et al (2013) Fault diagnosis of Tennessee Eastman process with multi-scale PCA and ANFIS. Chemom Intell Lab Syst 120:1–14

    Article  Google Scholar 

  16. Ouni K, Dhouibi H, Nabli L (2011) A new fault diagnosis method using fault directions in partial least square. IJCEST Vol 1(Issue 4):150–157

    Google Scholar 

  17. Jialin Q et al (2012) Neural network implementations for PCA and its extensions. Hindawi, Artif Intell vol 2012:1–19

    Google Scholar 

  18. Alcala CF, Qin SJ (2010) Reconstruction-based contribution for process monitoring with kernel principal component analysis. Ind Eng Chem Res 49(17):7849–7857

    Article  Google Scholar 

  19. KOURD Yahia, (2012)“Generation of residues by the tools of intelligence artificial systems for the diagnosis of complex system”, Ph.D. thesis, Annaba university

  20. Najeh T, Nabli L (2012) Development of a structuring residuals method for diagnostic by PCA-based genetic algorithms, In the Proceeding of 2nd International Conference on Communications, Computing and Control Applications (CCCA), pp. 1–6, Marseilles, 6–8 Dec. 2012

  21. Mohammad Abedinia, Mohammad Moradia H and Hosseinianb SM (2016) “Optimal clustering of MGs based on droop controller for improving reliability using a hybrid of harmony search and genetic algorithms”, Isa Transaction vol. 61:119–128

  22. Mohammad Jalali V et al (2012) A genetic algorithm with fuzzy crossover operator and probability. Hindawi, Adv Oper Res 2012:1–16

    Article  Google Scholar 

  23. Ehsan H, Ali M (2011) An efficient method based on genetic algorithms to solve sensor network optimization problem. Int J Appl Graph Theory Wirel Ad Hoc Netw Sens Netw (GRAPH-HOC) Vol.3(1):18–33

    Article  Google Scholar 

  24. Lixia M, Murroni M (2011) Peak-to-average power ratio reduction in multi-carrier system using genetic algorithms. IET Signal Process 5(3):356–363

    Article  Google Scholar 

  25. Rivero D et al (2012) Using genetic algorithms and k-nearest neighbour for automatic frequency band selection for signal classification. IET Signal Process 6(3):186–194

    Article  MathSciNet  Google Scholar 

  26. Janusz M, Venkatasubramanian V et al (1991) Automatic generation of qualitative description of ProcessTrends for fault detection and diagnosis. Eng Appl Artif Intell 4(5):329–339

  27. Rengaswamy R, Venkatasubramanian V (1995) A syntactic pattern recognition approach for process monitoring and fault diagnosis. Eng Appl Artif Intell 8(1):35–51

  28. Cheung JT (1992) “Representation and extraction of trends from process data”, Thèse de doctorat. Massachusetts Institute of Technology, Cambridge

  29. Lowe A, Harrison MJ, Jones RW (2016) “Diagnostic monitoring in anaesthesia using fuzzy trend templates for matching temporal patterns”, Artificial Intelligence in Medicine, pp.183–199

  30. Venkatasubramanian V, Vaidyanathan R, Yamamoto Y (1990) Process fault detection and diagnosis using neural networks: steady state processes. Comput Chem Eng 14(7):699–712

  31. Gertler J, McAvoy T (1997) Principal component analysis and parity relations - Astrong duality. IFAC Conference SAFEPROCESS, Hull, UK, pp. 837–842

  32. Qin SJ, Weihua L (1999) Detection, identification and reconstruction of faulty sensors with maximized sensitivity. AICHE J vol. 45(N ° 9):1963–1976

  33. Gertler J, Weihua L, Yunbing H and McAvoy T (1998) Isolation enhanced principal component analysis. 3rd IFAC Workshop on On-line Fault Détection and Supervision in the Chemical Process Industries, Lyon, June 4–5, France

  34. Bakshi BR (1998) Multiscale PCA with application to multivariate statistical process monitoring. AICHE Journal, 44(7):1596–1610

  35. Xu Y, Goodacre R (2012) Multiblock principal component analysis: an efficient tool for analyzing metabolomics data which contain two influential factors, Metabolomics. vol. 8(Suptl.1):37–51

  36. NL Ricker (1995) Optimal Steady state Operation of the Tennessee Eastman Challenge Process, Computers & Chemical Engineering, 19(9):949–959

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Authors and Affiliations

  1. Electric Engineering Department, Engineers School of Monastir, Ibn Aljazzar, Monastir, Tunisia

    Hanen Chaouch

  2. Electric Engineering Department, Engineers School of Monastir, Ibn Aljazzar, Monastir, Tunisia

    Tawfik Najeh

  3. Electric Engineering Department, Engineers School of Monastir, Ibn Aljazzar, Monastir, Tunisia

    Lotfi Nabli

Authors
  1. Hanen Chaouch
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  2. Tawfik Najeh
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  3. Lotfi Nabli
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Correspondence to Hanen Chaouch.

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Chaouch, H., Najeh, T. & Nabli, L. Multi-variable process data compression and defect isolation using wavelet PCA and genetic algorithm. Int J Adv Manuf Technol 91, 869–878 (2017). https://doi.org/10.1007/s00170-016-9774-y

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