A control chart pattern recognition system using a statistical ...

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J.-H. Yang, M.-S. Yang / Computers & Industrial Engineering 48 (2005) 205–221
Table 3d
Performance for different patterns with threshold hZ0.6
Input
Recognized pattern
u.s.
(%)
d.s.
(%)
u.t.
(%)
d.t.
(%)
cyc.
(%)
sys.
(%)
u.s.C
u.t.
(%)
d.s.C
d.t.
(%)
u.s.C
cyc.
(%)
d.s.C
cyc.
(%)
u.t.C
cyc.
(%)
d.t.C
cyc.
(%)
Normal
(%)
u.s. 70.5 0.0 0.0 0.0 0.0 0.0 24.0 0.0 1.5 0.0 0.0 0.0 4.0
d.s. 0.0 73.5 0.0 1.0 0.0 0.0 0.0 20.0 0.0 0.0 0.0 0.0 5.5
u.t. 0.0 0.0 70.0 0.0 0.0 0.0 17.5 0.0 0.0 0.0 0.5 0.0 12.0
d.t. 0.0 0.0 0.0 71.0 0.0 0.0 0.0 14.5 0.0 0.5 0.0 2.0 12.0
cyc. 0.0 0.0 0.0 0.0 72.5 0.0 0.0 0.0 0.0 0.0 1.0 1.5 25.0
sys. 0.0 0.0 0.0 0.0 0.0 86.0 0.0 0.0 0.0 0.0 0.0 0.0 14.0
u.s.Cu.t. 10.5 0.0 14.0 0.0 0.0 0.0 75.0 0.0 0.0 0.0 0.0 0.0 0.5
d.s.Cd.t. 0.0 15.5 0.0 12.5 0.0 0.0 0.0 72.0 0.0 0.0 0.0 0.0 0.0
u.s.Ccyc. 6.5 0.0 0.0 0.0 2.0 0.0 0.5 0.0 74.5 0.0 9.5 0.0 7.0
d.s.Ccyc. 0.0 4.5 0.0 1.0 2.0 0.0 0.0 3.5 0.0 69.0 0.0 12.0 8.0
u.t.Ccyc. 0.5 0.0 2.5 0.0 3.0 0.0 4.0 0.0 8.0 0.0 65.0 0.0 17.0
d.t.Ccyc. 0.0 0.0 0.0 5.0 4.0 0.0 0.0 0.0 0.0 10.5 0.0 70.0 10.5
Normal 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0
5. Conclusions
In this paper, we use a statistical correlation coefficient to create a simple mechanism for recognizing
single and concurrent unnatural patterns where a change point from normal to abnormal may occur in
control charts. By adding a threshold criterion, the proposed method becomes more powerful for
practical applications. The selection of a pattern length and a threshold was also discussed in this paper.
According to our simulations, the proposed statistical correlation coefficient method presents good
results. Tedious learning training process is not necessary for our method. The proposed mechanism
actually enhances the ability of recognition so that it helps us discover irregularities in a manufacturing
process as early as possible to reduce flawed products. Overall, the proposed approach is a simple
mechanism for recognizing unnatural patterns in control charts.
Acknowledgements
The authors are grateful to the anonymous referees for their helpful comments and suggestions to
improve the presentation of the paper. This work was supported in part by the National Science Council
of Taiwan, ROC, under Grant NSC-89-2213-E-033-057.
References
Al-Ghanim, A. M., & Ludeman, L. C. (1997). Automated unnatural pattern recognition on control charts using correlation
analysis techniques. Computers and Industrial Engineering, 32(3), 679–690.
Cheng, C. S. (1997). A neural network approach for the analysis of control chart patterns. International Journal of Production
Research, 35, 667–697.