OPEN ACCESS
A detection method of abrupt variations and changes in the evolution of a greatness in a process is presented. For that, at first the complexity of production processes in the semiconductor industry and the resulting difficulties in terms of quality control are described. The proposed detection method is used to observe changes in intrinsic variables of a product being produced or of a process. These changes could not be observed by classical control charts. This automatic method, inspired from signal processing, allows to model, estimate and predict the state of the process. It also allows to develop a break detection model based on hypothesis tests. These latter use the predicted state and the current state of the process to conclude on the presence of a variation corresponding to the detection of an event. This detection may motivate a decision making or prevent a dysfunction in the process.
detection, abrupt change, prediction, ARMA, hypothesis tests
Aroian L.A., Levene H., (1950). The effectiveness of quality control procedures. Jal American Statistical Association, vol. 45, p. 520-529.
Barnard G.A. (1959). Control charts and stochastic processes. Jal Royal Statistical Society, vol. B.21, p. 239-271.
Basseville M., (1988). Detecting Changes in Signals and Systems-A Survey. Automatica, vol. 24, n° 3, p. 309-326.
Basseville M., Nikiforov I.V. (1993). Detection of Abrupt Changes: Theory and Application. Prentice Hall
Box G., Narasimhan S., (2010). Rethinking Statistics for Quality Control, Quality Engineering, vol. 22, n° 2, p. 60-72.
Chakraborti S., Human S. W., Graham M. A. (2008). Phase I Statistical Process Control Charts: An Overview and Some Results, Quality Engineering, vol. 21, n° 1, p. 52-62.
Gibra I.N. (1975). Recent developments in control charts techniques. Jal Quality Technology, vol. 7, p. 183-192.
Landau I.D., M’Sirdi N.K., M’Saad M. (1986). Techniques de modélisation récursives pour l’analyse spectrale paramétrique adaptative. Traitement du Signal, vol. 3, n° 4-5.
M’Sirdi N.K., Ramdani R., Delanne Y. (1991). Détection de ruptures et application à la caractérisation de signaux de chaussée. 13e Colloque GRETSI, Juan-Les-Pins, 16-20 Septembre.
Pillet M. (2000). Appliquer la Maîtrise Statistique des Procédés. Les éditions d’organisation.
Pujo P., Pillet M. (2002). Control by quality: proposition of a typology. International Journal of Quality Assurance: Good Practice, Regulation, and Law, vol. 9, n° 2, p. 99-126.
Shewhart W.A. (1931) Economic Control of Quality of Manufactured Products. New York:Van Nostrand
Taylor H.M. (1967). Statistical control of a gaussian process. Technometrics, vol. 9, n° 1, p. 29-41.
Trip A., Does R.J.M.M. (2010). Quality Quandaries: Interpretation of Signals from Runs Rules in Shewhart Control Charts, Quality Engineering, vol. 22, n° 4, p. 351-357.
Vachette JL. (1990). Amélioration continue de la qualité SPC. Les éditions d’organisations.
Vance L.C. (1983). A bibliography of statistical quality control charts techniques, 1970-1980. Jal Quality Technology, vol. 15, p. 59-62.
Ycart B. (2002). Tests statistiques, Cahier de Mathématiques Appliquées, n° 6, p. 51-107.
Zacharewicz G., Pujo P., Frydman C., Giambiasi N. (2009). Environnement G-DEVS/HLA pour la simulation distribuée de systèmes de production multiprocessus. Journal of Decision Systems, vol. 18, n° 3, p. 375-402.