Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Joint monitoring of process mean and variance
Proceedings of second world congress on Nonlinear analysts
A neural network based model for abnormal pattern recognition of control charts
Computers and Industrial Engineering
Using recurrent neural networks to detect changes in autocorrelated processes for quality monitoring
Computers and Industrial Engineering
A hybrid learning-based model for on-line detection and analysis of control chart patterns
Computers and Industrial Engineering
Urinary nucleosides as potential tumor markers evaluated by learning vector quantization
Artificial Intelligence in Medicine
Computers and Industrial Engineering
Computers and Industrial Engineering
Computers and Industrial Engineering
Hi-index | 0.00 |
Control chart patterns (CCPs) can be employed to determine the behavior of a process. Hence, CCP recognition is an important issue for an effective process-monitoring system. Artificial neural networks (ANNs) have been applied to CCP recognition tasks and promising results have been obtained. It is well known that mean and variance control charts are usually implemented together and that these two charts are not independent of each other, especially for the individual measurements and moving range (X-R"m) charts. CCPs on the mean and variance charts can be associated independently with different assignable causes when corresponding process knowledge is available. However, ANN-based CCP recognition models for process mean and variance have mostly been developed separately in the literature with the other parameter assumed to be under control. Little attention has been given to the use of ANNs for monitoring the process mean and variance simultaneously. This study presents a real-time ANN-based model for the simultaneous recognition of both mean and variance CCPs. Three most common CCP types, namely shift, trend, and cycle, for both mean and variance are addressed in this work. Both direct data and selected statistical features extracted from the process are employed as the inputs of ANNs. The numerical results obtained using extensive simulation indicate that the proposed model can effectively recognize not only single mean or variance CCPs but also mixed CCPs in which mean and variance CCPs exist concurrently. Empirical comparisons show that the proposed model performs better than existing approaches in detecting mean and variance shifts, while also providing the capability of CCP recognition that is very useful for bringing the process back to the in-control condition. A demonstrative example is provided.