Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Neural Network Ensemble Approach for the Recognition of SPC Chart Patterns
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Features extraction and analysis for classifying causable patterns in control charts
Computers and Industrial Engineering
A hybrid learning-based model for on-line detection and analysis of control chart patterns
Computers and Industrial Engineering
Computers and Industrial Engineering
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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.