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EnglishThe traditional use of control charts assumes the independence of data. It is widely recognized that many processes are autocorrelated thus violating the main assumption of independence. As a result, there is a need for a broader approach to quality monitoring when data are time-dependent or autocorrelated. The aim of this work is to present a new procedure for manufacturing process quality control in the case of serially correlated data. In particular, a recurrent neural network is introduced for quality control problem. Performance comparisons between the neural-based algorithm and control charts are also presented in the paper in order to validate the proposed approach. The simulation results indicate that the neural-based procedure is quite effective as it achieves improved performance over control charts.
| Titolo: | Detecting Changes in Autoregressive Processes with a Recurrent Neural Network for Manufacturing Quality Monitoring | |
| Autori: | ||
| Data di pubblicazione: | 2005 | |
| Serie: | ||
| Abstract: | The traditional use of control charts assumes the independence of data. It is widely recognized that many processes are autocorrelated thus violating the main assumption of independence. As a result, there is a need for a broader approach to quality monitoring when data are time-dependent or autocorrelated. The aim of this work is to present a new procedure for manufacturing process quality control in the case of serially correlated data. In particular, a recurrent neural network is introduced for quality control problem. Performance comparisons between the neural-based algorithm and control charts are also presented in the paper in order to validate the proposed approach. The simulation results indicate that the neural-based procedure is quite effective as it achieves improved performance over control charts. | |
| Handle: | http://hdl.handle.net/11587/438880 | |
| ISBN: | 978-3-211-26537-6 | |
| Appare nelle tipologie: | Capitolo di Libro |
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