Application of integrated recurrent neural network with multivariate adaptive regression splines on SPC-EPC process
| Autor: | Ling-Jing Kao, Chih-Chou Chiu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Multivariate statistics Multivariate adaptive regression splines business.industry Computer science Feature selection 02 engineering and technology Statistical process control Machine learning computer.software_genre Industrial and Manufacturing Engineering Random forest 020901 industrial engineering & automation Recurrent neural network Hardware and Architecture Control and Systems Engineering Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Control chart Artificial intelligence business computer Software |
| Zdroj: | Journal of Manufacturing Systems. 57:109-118 |
| ISSN: | 0278-6125 |
| DOI: | 10.1016/j.jmsy.2020.07.020 |
| Popis: | The integration of statistical process control and engineering process control has been reported as an effective way to monitor and control the autocorrelated process. However, because engineering process control compensates for the effects of underlying disturbances, the disturbance patterns become very hard to recognize, especially when various abnormal control chart patterns are mixed and co-existed in the engineering process. In this study, a new control chart pattern recognition model which integrates multivariate adaptive regression splines and recurrent neural network is proposed to not only address the problem of feature selection (i.e., lagged process measurements) but also improve the pattern recognition accuracy. The performance of the proposed method is evaluated by comparing the recognition results of multivariate adaptive regression splines and recurrent neural network with the results of four competing approaches (multivariate adaptive regression splines-extreme learning machine, multivariate adaptive regression splines-random forest, single recurrent neural network, and single random forest) on the simulated individual process data. The experimental study shows that the proposed multivariate adaptive regression splines and recurrent neural network approach can not only solve the problem of variable selection but also outperform other competing models. Moreover, according to the lagged process measurements selected by the proposed approach, lagged observations that exerted significant impact on the construction of the control chart pattern recognition model can be identified successfully. This study has significant implications for research and practice in production management and provides a valuable reference for manufacturing process managers to better understand and develop strategies for control chart pattern recognition. |
| Databáze: | OpenAIRE |
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