توسعه یک روش آماری برای نمودار کنترل اندازه‌گیری‌های انفرادی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، دانشگاه آزاد اسلامی واحد فیروزکوه.

2 استادیار، دانشگاه شهید بهشتی.

3 استادیار، دانشگاه قم.

4 دکتری، دانشگاه آزاد اسلامی، واحد همدان.

چکیده

وقتی توزیع آماری محصولات تحت مطالعه، نرمال یا متقارن نباشد، موقع استفاده از نمودار کنترل X نمی‌توان انتظار داشت که تغییرپذیری فرایند به‌موقع کشف شود. در پژوهش حاضر برای بهبود عملکرد نمودار از توزیع لامبدای تعمیم‌یافته (GLD) استفاده شده است. برای نشان‌دادن نحوه‌ کار آن از داده‌های مربوط به مقاومت کششی 18 صفحه‌ آلومینیومی استفاده شده و حدود بالا و پایین نمودار کنترل X محاسبه گردیده است. برای اطمینان از جواب‌های به‌دست‌آمده از آزمون مربع کای استفاده شده است؛ و برای اعتبارسنجی معیار متوسط طول اجرا (ARL) به­کار رفته است. به دلیل انعطاف این توزیع، استفاده از روش پیشنهادی می‌تواند این اطمینان را فراهم آورد که بتوان تغییرپذیری‌ فرایند را زودتر کشف نمود.

کلیدواژه‌ها


عنوان مقاله [English]

Developing a Statistical Method for Control Chart of the Individual Measurements

نویسندگان [English]

  • Mohammad Mehdi Movahedi 1
  • Abbas Raad 2
  • Majid Nili Ahmadabadi 3
  • Behzad Ghasemi 4
1 Assistant Professor, Islamic Azad University, Firoozkooh Branch.
2 Assistant Professor, Shahid Beheshti University.
3 Assistant Professor, Qom University.
4 PhD., Islamic Azad University, Hamedan Branch.
چکیده [English]

When the statistical distribution of products under study is not normal or symmetrical, in order to use the X control chart it cannot be expected that the process variability is timely detected. In this study, to improve the performance of X control chart, Generalized Lambda Distribution (GLD) was applied. To illustrate how to implement the proposed method, the data pertaining to tensile straights of eighteen aluminum plates were utilized and upper and lower limits of X control chart were calculated. Chi-Square test and Average Run Length (ARL) method were employed to ensure the obtained results and to accredit the proposed method, respectively. Due to the flexibility of this distribution, applying the proposed method could ensure that the process variability is discovered ahead of time.

کلیدواژه‌ها [English]

  • X Control Chart
  • Generalized Lambda Distribution (GLD)
  • Percentile Matching (PM) Method
1. Albers, W., Kallenberg, W.C.M., & Nurdiati, S. (2004). Parametric control charts. J. Stat. Plann. Inf. 124, 159-184.
2. Alem Tabriz, A., Hamidizade, M. R., Dari Nokarani, B., & Mohammadi Plarti, M. (2017). A New product development model in the automotive industry. Journal of Industrial Management Perspective. 26(2), 33-51 (In Persian).
3. Azar, A., & Momeni, M. T. (2000). Statistics and its use in management, Organization for the Study and Compilation of Human Sciences Books of Universities, Tehran, Tran (In Persian).  
4. Bakir, S. T. (2004). A distribution-free Shewhart quality control chart based on signed-ranks. Qual. Eng. 16(4), 613-623.
5. Bashiri, M., Amiri, A., Doroudyan, M. H., & Asgari, A. (2013). Multi-objective genetic algorithm for economic statistical design of X bar control chart, Scientia Iranica, Sharif University of Technology, E 20 (3), 909-918.
6. Bertapelli, F., Machado M. R., Val Roso, R., & Guerra-Júnior, G. (2017). Body mass index reference charts for individuals with Down syndrome aged 2-18 years. Journal of Pediatr (Rio J)., 93(1), 94-99.
7. Borror, C. M., Montgomery, D. C., & Runger, G. C. (1999). Robustness of the EWMA control chart to non-normality. Journal of quality technology, 31(3), 309-316.
8. Chakraborti, S. (2000). Run length, average run length and false alarm rate of Shewhart x chart: exact derivations by conditioning. Commun. Stat. Simul. Comput. 29, 61-81.
9. Chandra M. Jeya, (2001). Statistical quality control, CRC Press LLC, 5-53.
10. Chang, Y. S., & Bai, K. C. (2001). Control charts for positively skewed populations with weighted standard deviations. Qual. Reliab. Eng. Int. 17, 397-406.
11. Chen, Y. K. (2003). An evolutionary economic-statistical design for VSI X control charts under non-normality. Int J Adv Manuf Technol, 22, 602-610.
12. Chen Y. K., & Yeh, C. (2004). An enhancement of DSI X Bar control charts using a fuzzy-genetic approach. Int J Adv Manuf Technol, 24, 32-40.
13. Dele H., & Besterfield P. E. (1998). Quality control fifth edition, Prentice Hall, New Jersey, Colombo, Ohio.
14. Devroye, L. (1996). Random variate generation in one line of code. In: Charnes, J. M., Morrice, D. J., Brunner, D. T., Swain, J. J., eds. Proceedings of the Winter Simulation Conference. San Diego, CA, USA, December 8-11. Association for Computing Machinery, NY, 265-272.
15. Fadaee, S., Pouya, A., & Kazemi, M. (2015). Developing fuzzy statistical process charts of defects ratio for control of descriptive characteristics. Journal of Industrial Management Perspective. 19(3), 91-116 (In Persian).
16. Farnum, N. R. (1994). Statistical quality control and improvement. Duxbury, Belmont.
17. Fatemi Qomi, M. T. (1991). Statistical quality control, Amir Kabir industrial University, Tehran, Iran (In Persian).
18. Fawad Zafara, R., Mahmoodb, C. T., Abbasb, N., Riazb, M., & Hussain, Z. (2018). A progressive approach to joint monitoring of process parameters. Computers & Industrial Engineering 115, 253-268.
19. Freimer, M., Mudholkar, S., Kollia, G., & Lin, T.C., (1988). A study of the generalized Tukey Lambda family. Commun. Statist. Theor. Meth. 17, 3547-3567.
20. Gilchrist, W. (2000). Statistical Modeling with Quantile Function. Boca Raton, FL: CRC Press.
21. Gunter, B. (1989). The use and abuse of c, part l-4. Quality progress, part 1: 22 (1), 72-73, part 2: 22(3), 108-109, part 3: 22(5), 79-80, and part 4: 22(7), 86-87.
22. Harrison, M., Wadsworth, J. R., Kennets, S., Stephens, A., & Blanton Godfrey (2001). Modern methods for quality control and improvement. John wiley & sons, INC, 243-245.
23. Hoaglin, D. C. (1975). The small-sample variance of the Pitman location estimators. J. Amer. Statist. Assoc. 52, 880-888.
24. Hitchina, R., & Knight, I. (2016). Daily energy consumption signatures and control charts forair-conditioned buildings. Energy and Buildings 112, 101-109.
25. Hsu, H. M., & Chen Y. K. (2001). A fuzzy reasoning based diagnosis system for X bar control charts. Journal of Intelligent Manufacturing, 12, 57-64.
26. Jang S., Park S.H., and Baek J.G., (2017). Real-time contrasts control chart using random forests with weighted voting. Expert Systems with Applications 71, 358-369.
27. Joiner, B. L., & Rosenblatt, J. R. (1971). Some properties of the range in samples from Tukeys symmetric lambda distribution. J. Amer. Statist. Assoc. 66, 394-399.
28. Karian, Z. A., Dudewicz, E. J. (2000). Fitting Statistical Distributions. The Generalized Lambda Distribution and Generalized Bootstrap Methods. Boca Raton, FL: CRC Press.
29. Kariya, T. (1986). Analogous t and F test statistics based on grouped data. In: Francis, I. S., Manly, B. F. J., Lam, F. C., eds. Proceedings of the Pacific Statistical Congress, Auckland, 20–24, May 1985. Elsevier Science Publishers B.V. (North-Holland), 275-279.
30. Katz, S., & Johnson, N. L. (1995). Process Capability Indices. Chapman and Hall, New York.
31. Kaya, I., & Kahraman, C. (2011). Process capability analyses based on fuzzy measurements and fuzzy control charts. Expert Syst Appl 38, 3172-3184.
32. Kittlitz, R. G., (1999). Transforming the exponential for spc applications. J. Qual. Technol. 31, 301-308.
33. Lam, H., Bowman, K. O., & Shenton, L. R. (1980). Remarks on the generalized Tukeys lambda family of distributions. In: Proc. ASA, Statist. Comput. Sec. Houston, Texas, August 11–14, 134-139.
34. Lee, S. J., & Amin, R. W. (2000). Process tolerance limits. Total Qual. Manag. 11(3), 267-280.
35. Maddahi A., Shahriari H., & Shokouhi A. H. (2011). A robust X control chart based on M-estimators in presence of outliers. Int J Adv Manuf Technol, 56, 711-719.
36. Maravelakis, P. E., Panaretos, J., & Psarakis, S. (2002). Effect of estimation of the process parameters on the control limits of the univariate control charts for process dispersion. Commun. Stat.: A. 31(3), 443-461.
37. Michael, B., & Khoo, C. (2004). Performance measures for the Shewhart x control chart. Qual. Eng. 16(4), 585-590.
38. Montgomery, D. C., (2001). Introduction to statistical quality control, 4th edn. John Wiley & Sons, New York.
39. Montgomery, Jennings, & Pfund, (2010). Management controlling and improving quality, John Wiley.
40. Moatamevi, A., Rezaee, M., & Ehghaghi, M. (2013). Designing demand forecasting model in the ceramic and tile industry. Journal of Industrial Management Perspective. 9(1), 159-177 (In Persian).
41. Movahedi, M. M., & Bamenimoghaddam, M. (2011). Statistical quality control, Publication of Sharh, Tehran, Iran, (In Persian).
42. Movahedi, M. M., Khounsiavash, M., Otadi, M., & Mosleh, M. (2016). A new statistical method for design and analyses of component tolerance. J Ind Eng Int., DOI 10.1007/s40092-016-0167-5.
43. Nedumaran, G., Pignatiello, Jr, J.J., (2001). On estimating x control chart limits. J. Qual. Technol. 33, 206-212.
44. Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. Comput. J. 7, 308-313.
45. Nil, N., Rao Kraleti, S. (2010). Optimal design of X bar-control chart with Pareto in-control times. Int J Adv Manuf Technol, 48, 829-837.
46. Nili Ahmadabadi, M., Farjami, Y., & BameniMoghadam, M. (2011). A process control method based on five-parameter generalized lambda distribution. Qual Quant. Springer Science+Business Media B.V. 46, 1097-1111.
47. Nili Ahmadabadi, M., Farjami, Y., M., & BameniMoghadam, M. (2012). Approximating Distributions by Extended Generalized Lambda Distribution (XGLD). Communications in Statistics—Simulation and Computation, 41, 1-23.
48. Noghondarian, K. (2005). Statistical quality control, Science and Technology University of Iran, Tehran, Iran, in Persian.
49. Norolsana, R., (2000). Statistical quality control, Science and Technology University of Iran, Tehran, Iran, in Persian.
50. Olsson, D. M., & Nelson, L. S. (1975). The Nelder-Mead simplex procedure for function minimization. Technometrics 17, 45-51.
51. O’Neill, B., Wells, W. T. (1972). Some recent results in lognormal parameter estimation using grouped and ungrouped data. J. Amer. Statist. Assoc. 167, 76-80.
52. Peam, W. L., Kotz, S., & Johnson, N. L., (1992). Distributional and inferential properties of process capability indices. J. Qual. Technol. 24(4), 216-231.
53. Pearson, R. K. (2001). Exploring process data. J. Process Control 11, 179-194.
54. Pelegrina, G. D., Leonardo, Duarte, T., & Jutten, C. (2016). Blind source separation and feature extraction in concurrent control charts pattern recognition: Novel analyses and a comparison of different methods. Computers & Industrial Engineering 92, 105-114.
55. Pérez-Rave, J., Muñoz-Giraldo, L., & Correa-Morales, J. C. (2017). Use of control charts with regression analysis for autocorrelated data in the context of logistic financial budgeting. Computers & Industrial Engineering 112, 71-83.
56. Prajapati, D. R. (2003). Performance of Conventional X-bar Chart for Auto correlated Data Using Smaller Sample Sizes. In: Proceedings of the World Congress on Engineering and Computer Science 2013 Vol II, October 23-25, 2013, San Francisco, USA.
57. Pyzdek, T. (1993). Processes control for short and small runs. Qual. Prog. 12, 51-60.
58. Pyzdek, T. (1995). Why normal distributions aren’t (all that normal). Qual. Eng. 7, 769-777.
59. Quesenberry, C. P. (1993). The effect of sample size on estimated limits for Xbar and X control charts. J Qual Technol 25, 237-247.
60. Ramberg, J. S., & Schmeiser, B. W. (1974). An approximate method for generating asymmetric random variables. Commun. ACM 17, 78-82.
61. Reynolds, M. R. Jr, Amin, R. W., Arnold, J. C., & Nachlas, J. A. (1988). X charts with variable sampling interval. Technometrics 30, 181-192.
62. Riaz, M. (2008). A dispersion control chart. Common. Stat.: Simul. Computat. 37, 1239-1261.
63. Sadeghpour Gildeh, B., Niloufar Shafiee N., (2015). X-MR control chart for auto correlated fuzzy data using Dp, q-distance. Int J Adv Manuf Technol, 81, 1047-1054.
64. Sarabia, J. M. (1996). A hierarchy of Lorenz curves based on generalized Tukeys lambda distribution. Econometric Rev. 16, 305-320.
65. Shapiro, S. S., Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika 52, 591-611.
66. Shewhart, W. A., (1931). Economic Control of Quality of Manufactured Product, Van Nostrand, New York.
67. Somerville, S. E., Montgomery, D. C., (1996). Process capability indices and non-normal distributions. Qual. Eng. 9(2), 305-316.
68. Tarsitano, A., (2005). Estimation of the Generalized Lambda Distribution Parameters for Grouped Data. Taylor & Francis, Inc., Communications in Statistics, Theory and Methods, 34, 1689-1709.
69. Teyarachakul, S., Chand, S., & Tang, J. (2007). Estimating the limits for statistical process control charts: a direct method improving upon the bootstrap. Eur. J. Oper. Res. 178, 472-481.
70. Tsai, T. R., Lin, J. J., Wu, S. J., & Lin, H. C. (2005). On estimating control limits of X bar chart when the number of subgroups is small. Int J Adv Manuf Technol, 26, 1312-1316.
71. Tukey, J.W., (1962). The future of data analysis. annals of mathematical statistics, 33(1), 1-67.
72. Wiemken T. L., Carrico R. M., Persaud A. K., & Ramirez J. A. (2017). Process control charts in infection prevention: Make it simple to make it happen. American Journal of Infection Control 45, 216-21.
73. Woodall, W. H., (2000). Controversies and contradictions in statistical process control. J. Qual. Technol. 32(4), 341-350.
74. Yang, S.-F., (1999). An approach to controlling process variability for short production runs. Total Quality Management. 10(8), 1123-1129.
75. Yourstone, S., & Zimmer, W., (1992). Non-normality and the design of control charts for averages. Decis. Sci. 23, 1099-1113.
76. Zhou, W., Zheng, Z., & Xie, W., (2017). A control-chart-based queueing approach for service facility maintenance with energy-delay tradeoff. European Journal of Operational Research 261, 613-625.