Computers and Industrial Engineering
Intelligent Quality Systems
Design and Analysis of Experiments
Design and Analysis of Experiments
Advanced Engineering Informatics
Applying back propagation network to cold chain temperature monitoring
Advanced Engineering Informatics
Applying ICA and SVM to mixture control chart patterns recognition in a process
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Computers and Industrial Engineering
Recognition of control chart patterns using an intelligent technique
Applied Soft Computing
Journal of Intelligent Manufacturing
Computers and Industrial Engineering
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Any nonrandom patterns shown in Statistical Process Control (SPC) charts imply possible assignable causes that may deteriorate the process performance. Hence, timely detecting and recognizing Control Chart Patterns (CCPs) for nonrandomness is very important in the implementation of SPC. Due to the limitations of run-rule-based approaches, Artificial Neural Networks (ANNs) have been resorted for detecting CCPs. However, most of the reported ANN approaches are only limited to recognize single basic patterns. Different from these approaches, this paper presents a hybrid approach by integrating wavelet method with ANNs for on-line recognition of CCPs including concurrent patterns. The main advantage of this approach is its capability of recognizing coexisted or concurrent patterns without training by concurrent patterns. The test results using simulated data have demonstrated the improvements and the effectiveness of the methodology with a success rate up to 91.41% in concurrent CCP recognition.