کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1134574 | 956072 | 2011 | 10 صفحه PDF | دانلود رایگان |
Unnatural patterns exhibited in manufacturing processes can be associated with certain assignable causes for process variation. Hence, accurate identification of various process patterns (PPs) can significantly narrow down the scope of possible causes that must be investigated, and speed up the troubleshooting process. This paper proposes a Gaussian mixture models (GMM)-based PP recognition (PPR) model, which employs a collection of several GMMs trained for PPR. By using statistical features and wavelet energy features as the input features, the proposed PPR model provides more simple training procedure and better generalization performance than using single recognizer, and hence is easier to be used by quality engineers and operators. Furthermore, the proposed model is capable of adapting novel PPs through using a dynamic modeling scheme. The simulation results indicate that the GMM-based PPR model shows good detection and recognition of current PPs and adapts further novel PPs effectively. Analysis from this study provides guidelines in developing GMM – based SPC recognition systems.
► Abnormal process patterns are associated with assignable causes for process variation.
► We developed a Gaussian mixture models-based process pattern recognition model.
► The proposed model provides a simple training procedure and good performance.
► The proposed model is capable of adapting novel process patterns.
Journal: Computers & Industrial Engineering - Volume 61, Issue 3, October 2011, Pages 881–890