An integrated system for on-line intelligent monitoring and identifying process variability and its application
An integrated system for on-line intelligent monitoring and identifying process variability and its application
复制标题
过程变异在线智能监测与识别集成系统及其应用
DOI：
10.1080/09511921003667730
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发表时间：
2010-06
影响因子：
4.1
通讯作者：
Xi, L.
中科院分区：
文献类型：
--
作者：
Du, S.;Lv, J.;Xi, L.
为了减少复杂制造过程中的过程可变性，存在将过程可变性（PV）监测和失控信号源（SOS）识别集成的巨大需求。先进的测量和信息技术的出现为提高产品质量提供了有希望的机会。本文研究了一种多变量制造过程PV智能监测与SOS识别集成系统。|S|控制图作为异常信号的检测器和改进的粒子群优化与模拟退火为基础的选择性神经网络集成（PSOSAEN）识别SOS探索。控制图和PSOSAEN的无缝集成提供了异常警告，揭示SOS，并帮助操作员采取一些必要的纠正和调整。通过一个真实的应用实例验证了所开发的集成系统的实用性和有效性。分析结果表明，所开发的集成系统可以有效地监测和分类方差增加。该研究为开发基于集成神经网络集成的MMPs多元统计过程控制辨识系统提供了指导。
To reduce process variability in complex manufacturing processes, a tremendous need exists to integrate monitoring process variability (PV) and identification of source of out-of-control signals (SOS). The advent of advanced measurement and information technology has provided promising opportunities to improve product quality. In this paper, one integrated system is explored for intelligent monitoring PV and identifying of SOS in multivariate manufacturing processes (MMPs). |S| control chart is used as the detector of abnormal signals and an improved particle swarm optimisation with simulated annealing-based selective neural network ensemble (PSOSAEN) is explored for identifying the SOS. The seamless integration of control chart and PSOSAEN provides abnormal warnings, reveals SOS and helps operators to take some necessary corrections and adjustments. A real application is illustrated to validate the usefulness and effectiveness of the developed integrated system. The analysis results indicate that the developed integrated system can perform effectively for monitoring and classifying variance increases. This study provides guidelines for developing integrated neural network ensemble-based multivariate statistical process control identification systems in MMPs.
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DOI：
10.1145/2598394.2605342
发表时间：
2014-07
期刊：
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
影响因子：
--
作者：
A. Engelbrecht
通讯作者：
A. Engelbrecht
DOI：
10.1016/b978-0-12-409547-2.14581-0
发表时间：
2020
期刊：
Comprehensive Chemometrics
影响因子：
--
作者：
Federico Marini;Beata Walczak
通讯作者：
Federico Marini;Beata Walczak
DOI：
10.1007/978-3-319-09776-3_4
发表时间：
2014
期刊：
--
影响因子：
--
作者：
R. Kern
通讯作者：
R. Kern
DOI：
10.1201/9781003206477-5
发表时间：
2021-08
期刊：
Evolutionary Optimization Algorithms
影响因子：
--
作者：
A. Badar
通讯作者：
A. Badar
影响因子：
2.5
作者：
T. McAvoy
通讯作者：
T. McAvoy