Abstract
There is a great challenge in carrying out multivariate process capability analysis and fault diagnostics on a high dimensional non-normal process, with multiple correlated quality characteristics, in a timely manner. This paper proposes a hybrid capable of performing process capability analysis and fault diagnostics on multivariate non-normal processes. The proposed hybrid first utilizes the Geometric Distance (GD) approach, to reduce dimensionality of the correlated data into fewer number of independent GD variables which can be assessed using univariate process capability indices (PCIs). This is followed by fitting Burr XII distribution to independent GD variables. The independent fitted distributions are used to estimate both yield and multivariate process capability in a time efficient way. Finally, machine learning approach, is deployed to carry out the task of fault diagnostic by identifying and ranking the correlated quality characteristics responsible for the poor performance of individual GD variables. The efficacy of the proposed hybrid is assessed through a real manufacturing example and four simulated scenarios. The results show that the proposed hybrid is robust in estimating both yield and multivariate process capability carrying out fault diagnostics beyond GD variables, and identifying the original characteristic responsible for poor performance.
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References
Shahriari, H., Hubele, N.F., Lawrence, F.P.: A multivariate process capability vector. Proceedings of The 4th industrial engineering research conference, Institute of Industrial Engineers. pp. 304–309, (1995)
Taam, W., Subbaiah, P., Liddy, J.W.: A note on multivariate capability indices. J. Appl. Stat. 20, 339–351 (1993)
Ahmad, S., Abdollahian, M., Zeephongsekul, P., Abbasi, B.: Multivariate non-normal process capability analysis. Int. J. Adv. Manuf. Technol. 44, 757–765 (2009)
Gunaratne, N.G.T., Abdollahian, M.A., Huda, S., Yearwood, J.: Exponentially weighted control charts to monitor multivariate process variability for high dimensions. Int. J. Prod. Res. 55(17), 4948–4962 (2017)
Hsu, C., Huang, H., Schuschel, D.: The ANNIGMA-Wrapper approach to fast feature selection for neural nets. IEEE Trans. Syst. Man Cybern. B Cybern. 32, 207–212 (2002)
Dharmasena, L., Zeephongsekul, P.: A new process capability index for multiple quality characteristics based on principal components. Int. J. Prod. Res. 54, 4617–4633 (2016)
Wang, F.K.: Quality evaluation of a manufactured product with multiple characteristics. Qual. Reliab. Eng. Int. 22, 225–236 (2005)
de-Felipe, D., Benedito, E.: A review of univariate and multivariate process capability indices. Int. J. Adv. Manuf. Technol. 92, 1687–1705 (2017)
Wang, F.K., Hubele, N.F.: Quality evaluation using Geometric Distance approach. Int. J. Reliab. Qual. Saf. Eng. 6, 139–153 (1999)
Burr, I.W.: Cumulative frequencu distribution. Ann. Math. Stat. 13, 215–232 (1942)
Liu, P.H., Chen, F.L.: Process capability analysis of non-normal process data using Burr XII distribution. Int. J. Adv. Manuf. Technol. 27, 975–984 (2009)
Shiau, J.H., Yen, C.L., Pearn, W.L., Lee, W.T.: Yield-related process capability indices for processes of multiple quality characteristics. Qual. Reliab. Eng. Int. 29, 487–507 (2013)
Huda, S., Abdollahian, M., Mammadov, M., Yearwood, J., Shafiq Ahmed, S., Sultan, I.: A hybrid wrapper–filter approach to detect the source(s) of out-of-control signals in multivariate manufacturing process. Eur. J. Oper. Res. 237, 857–870 (2014)
Headrick, T.C.: Fast fifth-order polynomial transforms for generating univariate and multivariate nonnormal distributions. Comput. Stat. Data Anal. 40, 685–711 (2002)
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Munjeri, D., Abdollahain, M., Gunaratne, N. (2019). Fault Diagnostics for Multivariate Non-normal Processes. In: Latifi, S. (eds) 16th International Conference on Information Technology-New Generations (ITNG 2019). Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-030-14070-0_30
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DOI: https://doi.org/10.1007/978-3-030-14070-0_30
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