.png){kind=icon}
返回
Applying emerging soft computing approaches to control chart pattern recognition for an SPC-EPC process
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2016.04.004.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
One of the primary tasks for process personnel is to detect and identify the underlying process disturbances such that they can be quickly removed. Most research has concluded that the integration of statistical process control (SPC) and engineering process control (EPC) is an effective way to achieve this task. Although this integration may lead to many benefits, it could result in problems with control chart pattern recognition. The EPC adjustments could cause the underlying disturbance patterns to be embedded in the control chart, thus dramatically increasing the degree of difficulty to identify the behavior of process disturbances. This study considers a zero-order autoregressive and integrated moving average process (ARIMA) that contains five common process disturbances. In addition, the minimum mean squared error (MMSE) control actions serve as the role of the EPC. In contrast to using the conventional soft computing methods, this study proposes two emerging soft computing techniques, extreme learning machine (ELM) and random forest (RF), to address the difficulties for recognition of embedded disturbance patterns in the control charts. Experimental results revealed that the proposed approaches are able to effectively recognize various disturbance patterns of an SPC-EPC process. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Control chart pattern
Artificial neural network
Extreme learning machine
Random forest
Rough set
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Photoacoustics, thermoacoustics, and acousto-optics for biomedical imaging.png)
Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine
Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in MedicineIF0
PREAI
分享收藏Room temperature synthesis of reduced graphene oxide nanosheets as anode material for supercapacitors室温合成还原氧化石墨烯纳米片作为超级电容器负极材料的研究
PREAI
分享收藏
Outlier-robust extreme learning machine for regression problems用于回归问题的离群鲁棒极限学习机NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容