عنوان مقاله [English]
The process control charts are most important tool of statistical process control (SPC) approach. The general control procedures of these charts only monitor charts' samples individually and do not consider the obtained common information from successive samples as probable potential disorders.
The existence of natural variations in the control charts is inevitable, but the appearances of significant patterns in these charts warn the special disturbances in production processes and associate out-of-control situations. The natural variations often divert significant patterns from their expected forms; therefore, increase of qualitative sensitivities level for study of unnatural patterns in the control charts is mandatory.
In resent years, to recognize and analyze non-random patterns in the process control charts, numerous models have been presented. These models usually cannot alarm the occurrences of various formations modes of cyclic and systematic patterns, since the periodic patterns have phase difference in their starting point and most of these researches merely have simulated one simple phase of their formation.
On the other hand, few developer models of periodic patterns generating functions have applied the artificial neural networks as recognition tool. Although the neural networks are capable in patterns learning, however they have difficult architectures, time-consuming algorithms and uncertain reliability when the sensitivities of processes to the appearances of significant patterns are high.
This paper introduces a new model based on fitted cosine curve of samples for more accurate discrimination of the various formations phases of cyclic and systematic patterns and more precise estimation of their corresponding parameters at different levels of sensitivity. Our proposed model compares all periodic alternatives, then selects the best fitted cosine curve of samples, and finally determines situation of process. The results of simulated tests indicate that the proposed model reduces the misclassification error of cyclic and systematic patterns and decreases the estimation error average of their corresponding parameters, in comparison with developer models of periodic patterns, for the various emergences states.