Neural Computing and Applications

, Volume 31, Issue 10, pp 5935–5949 | Cite as

Improved statistical features-based control chart patterns recognition using ANFIS with fuzzy clustering

  • Munawar ZamanEmail author
  • Adnan Hassan
Original Article


Various types of abnormal control chart patterns can be linked to certain assignable causes in industrial processes. Hence, control chart patterns recognition methods are crucial in identifying process malfunctioning and source of variations. Recently, the hybrid soft computing methods have been implemented to achieve high recognition accuracy. These hybrid methods are complicated, because they require optimizing algorithms. This paper investigates the design of efficient hybrid recognition method for widely investigated eight types of X-bar control chart patterns. The proposed method includes two main parts: the features selection and extraction part and the recognizer design part. In the features selection and extraction part, eight statistical features are proposed as an effective representation of the patterns. In the recognizer design part, an adaptive neuro-fuzzy inference system (ANFIS) along with fuzzy c-mean (FCM) is proposed. Results indicate that the proposed hybrid method (FCM-ANFIS) has a smaller set of features and compact recognizer design without the need of optimizing algorithm. Furthermore, computational results have achieved 99.82% recognition accuracy which is comparable to published results in the literature.


Control chart patterns recognition Fuzzy clustering ANFIS Statistical features 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest. Also, this research work has not been submitted for publication nor has it been published in part or in whole elsewhere. We verify to the fact that all authors listed on the title page have contributed significantly to the work, have read the document, confirm to the validity and legitimacy of the data and its explanation, and agree to its submission to the Journal of Neural Computing and Applications.


  1. 1.
    Montgomery DC (2009) Statistical quality control-a modern introduction. Wiley, New YorkzbMATHGoogle Scholar
  2. 2.
    Western Electric Company. (1958) Statistical quality control handbook. The CompanyGoogle Scholar
  3. 3.
    Nelson LS (1984) The Shewhart control chart–tests for special causes. J Qual Technol 16(4):237–239Google Scholar
  4. 4.
    Lucy-Bouler TL (1993) Problems in control chart pattern recognition systems. Int J Qual Reliab Manag 10(8):5–13Google Scholar
  5. 5.
    Guh RS (2005) A hybrid learning-based model for on-line detection and analysis of control chart patterns. Comput Ind Eng 49(1):35–62Google Scholar
  6. 6.
    Uğuz H (2012) Adaptive neuro-fuzzy inference system for diagnosis of the heart valve diseases using wavelet transform with entropy. Neural Comput Appl 21(7):1617–1628Google Scholar
  7. 7.
    Demircan S, Kahramanli H (2016) Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech. Neural Comput Appl.
  8. 8.
    Lavanya B, Hannah Inbarani H (2017) A novel hybrid approach based on principal component analysis and tolerance rough similarity for face identification. Neural Comput Appl.
  9. 9.
    Kazemi MS, Kazemi K, Yaghoobi MA, Bazargan H (2016) A hybrid method for estimating the process change point using support vector machine and fuzzy statistical clustering. Appl Soft Comput 40:507–516Google Scholar
  10. 10.
    Haykin SS (2001) Neural networks: a comprehensive foundation. Tsinghua University Press, BeijingzbMATHGoogle Scholar
  11. 11.
    Vapnik V (2013) The nature of statistical learning theory. Springer, BerlinzbMATHGoogle Scholar
  12. 12.
    Ebrahimzadeh A, Ranaee V (2011) High efficient method for control chart patterns recognition. Acta technica ČSAV 56(1):89–101Google Scholar
  13. 13.
    Xanthopoulos P, Razzaghi T (2014) A weighted support vector machine method for control chart pattern recognition. Comput Ind Eng 70:134–149Google Scholar
  14. 14.
    A Viattchenin D, Tati R, Damaratski A (2013) Designing Gaussian membership functions for fuzzy classifier generated by heuristic possibilistic clustering. J Inf Organ Sci 37(2):127–139Google Scholar
  15. 15.
    Zarandi MF, Alaeddini A, Turksen IB (2008) A hybrid fuzzy adaptive sampling–run rules for Shewhart control charts. Inf Sci 178(4):1152–1170Google Scholar
  16. 16.
    Khajehzadeh A, Asady M (2015) Recognition of control chart patterns using adaptive neuro-fuzzy inference system and efficient features. Int J Sci Eng Res 6(9):771–779Google Scholar
  17. 17.
    Khormali A, Addeh J (2016) A novel approach for recognition of control chart patterns: Type-2 fuzzy clustering optimized support vector machine. ISA Trans 63:256–264Google Scholar
  18. 18.
    Das P, Banerjee I (2011) An hybrid detection system of control chart patterns using cascaded SVM and neural network–based detector. Neural Comput Appl 20(2):287–296Google Scholar
  19. 19.
    Pham DT, Wani MA (1997) Feature-based control chart pattern recognition. Int J Prod Res 35(7):1875–1890zbMATHGoogle Scholar
  20. 20.
    Gauri SK, Chakraborty S (2009) Recognition of control chart patterns using improved selection of features. Comput Ind Eng 56(4):1577–1588Google Scholar
  21. 21.
    Hassan A, Baksh MSN, Shaharoun AM, Jamaluddin H (2011) Feature selection for SPC chart pattern recognition using fractional factorial experimental design. In: Pham DT, Eldukhri EE, Soroka AJ (eds) Intelligent production machines and system: 2nd I* IPROMS virtual international conference, Elsevier, pp 442–447Google Scholar
  22. 22.
    Hassan A, Baksh MSN, Shaharoun AM, Jamaluddin H (2003) Improved SPC chart pattern recognition using statistical features. Int J Prod Res 41(7):1587–1603Google Scholar
  23. 23.
    Al-Assaf Y (2004) Recognition of control chart patterns using multi-resolution wavelets analysis and neural networks. Comput Ind Eng 47(1):17–29Google Scholar
  24. 24.
    Cheng CS, Huang KK, Chen PW (2015) Recognition of control chart patterns using a neural network-based pattern recognizer with features extracted from correlation analysis. Pattern Anal Appl 18(1):75–86MathSciNetGoogle Scholar
  25. 25.
    Masood I, Hassan A (2010) Issues in development of artificial neural network-based control chart pattern recognition schemes. Eur J Sci Res 39(3):336–355Google Scholar
  26. 26.
    Hachicha W, Ghorbel A (2012) A survey of control-chart pattern-recognition literature (1991–2010) based on a new conceptual classification scheme. Comput Ind Eng 63(1):204–222Google Scholar
  27. 27.
    Swift JA (1987) Development of a knowledge-based expert system for control-chart pattern recognition and analysis. Oklahoma State Univ, StillwaterGoogle Scholar
  28. 28.
    De la Torre Gutierrez H, Pham DT (2016) Estimation and generation of training patterns for control chart pattern recognition. Comput Ind Eng 95:72–82Google Scholar
  29. 29.
  30. 30.
    Bennasar M, Hicks Y, Setchi R (2015) Feature selection using joint mutual information maximisation. Expert Syst Appl 42(22):8520–8532Google Scholar
  31. 31.
    Haghtalab S, Xanthopoulos P, Madani K (2015) A robust unsupervised consensus control chart pattern recognition framework. Expert Syst Appl 42(19):6767–6776Google Scholar
  32. 32.
    Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203Google Scholar
  33. 33.
    Deer PJ, Eklund P (2003) A study of parameter values for a Mahalanobis distance fuzzy classifier. Fuzzy Sets Syst 137(2):191–213MathSciNetzbMATHGoogle Scholar
  34. 34.
    Sumathi S, Surekha P, Surekha P (2010) Computational intelligence paradigms: theory and applications using MATLAB, vol 1. CRC Press, Boca RatonGoogle Scholar
  35. 35.
    Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685Google Scholar
  36. 36.
    Gacek A, Pedrycz W (eds) (2011) ECG signal processing, classification and interpretation: a comprehensive framework of computational intelligence. Springer, BerlinGoogle Scholar
  37. 37.
    Joaquim PMDS, Marques S (2007) Applied statistics using SPSS, statistica, Matlab and R. Springer, Berlin, pp 205–211zbMATHGoogle Scholar
  38. 38.
    Carbonneau R, Laframboise K, Vahidov R (2008) Application of machine learning techniques for supply chain demand forecasting. Eur J Oper Res 184(3):1140–1154zbMATHGoogle Scholar
  39. 39.
    Stukowski A (2009) Visualization and analysis of atomistic simulation data with OVITO—the Open Visualization Tool. Modell Simul Mater Sci Eng 18(1):015012MathSciNetGoogle Scholar
  40. 40.
    Mylonopoulos NA, Doukidis GI, Giaglis GM (1995). Assessing the expected benefits of electronic data interchange through simulation modelling techniques. In: The proceedings of the 3rd European conference on information systems, Athens, Greece, pp 931–943Google Scholar
  41. 41.
    Abdulmalek FA, Rajgopal J (2007) Analyzing the benefits of lean manufacturing and value stream mapping via simulation: a process sector case study. Int J Prod Econ 107(1):223–236Google Scholar
  42. 42.
    Assaleh K, Al-Assaf Y (2005) Features extraction and analysis for classifying causable patterns in control charts. Comput Ind Eng 49(1):168–181Google Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of Material, Manufacturing and Industrial Engineering, Faculty of Mechanical EngineeringUniversiti Teknologi MalaysiaSkudai, Johor BahruMalaysia

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