当前位置: X-MOL 学术J. Manuf. Syst. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Application of integrated recurrent neural network with multivariate adaptive regression splines on SPC-EPC process
Journal of Manufacturing Systems ( IF 12.1 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.jmsy.2020.07.020
Ling-Jing Kao , Chih Chou Chiu

Abstract 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.

中文翻译:

带多元自适应回归样条的集成递归神经网络在SPC-EPC过程中的应用

摘要 统计过程控制和工程过程控制的集成被认为是监测和控制自相关过程的有效方法。然而,由于工程过程控制补偿了潜在扰动的影响,扰动模式变得非常难以识别,特别是当各种异常控制图模式在工程过程中混合并共存时。在这项研究中,提出了一种新的控制图模式识别模型,它集成了多元自适应回归样条和循环神经网络,不仅可以解决特征选择(即滞后过程测量)问题,还可以提高模式识别精度。通过将多元自适应回归样条和循环神经网络的识别结果与四种竞争方法(多元自适应回归样条 - 极限学习机,多元自适应回归样条 - 随机森林,单循环神经网络)的结果进行比较来评估所提出方法的性能。网络和单个随机森林)模拟的单个过程数据。实验研究表明,所提出的多元自适应回归样条和循环神经网络方法不仅可以解决变量选择问题,而且优于其他竞争模型。此外,根据所提出的方法选择的滞后过程测量,可以成功识别对控制图模式识别模型的构建产生重大影响的滞后观察。本研究对生产管理的研究和实践具有重要意义,为制造过程管理人员更好地理解和制定控制图模式识别策略提供了宝贵的参考。
更新日期:2020-10-01
down
wechat
bug