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首页> 外文期刊>International Journal of Natural and Engineering SciencesbElectronic resource >On-line Analysis Out-of-Control Signals for Multivariate Control Chart Using Neural Network
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On-line Analysis Out-of-Control Signals for Multivariate Control Chart Using Neural Network

机译：使用神经网络对多元控制图进行在线分析失控信号

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摘要

It is common in industrial process to monitor several correlated quality variables simultaneously. Most of multivariate quality control charts are effective in detecting out-of-control signals based upon an overall statistics in multivariate manufacturing processes. The main problem of such charts is that they can detect an out-of-control event but do not directly determine which variable or group of variables has caused the out-of-control signal and what is the magnitude of out of control. This study presents an artificial neural network-based model to supplement the multivariate χ2 chart. This method consists of two modules. In the first module using a general-neural network, type of unnatural pattern can be recognized. Then by using two special-neural networks for shift mean and trend, it can be recognized magnitude of mean shift and slope of trend for each variable simultaneously. The performance of the proposed approach has been evaluated using a simulated example. The results confirm that the proposed method provides an excellent rate of classification and the output generated by trained network is strongly correlated with the corresponding actual target value for each quality characteristic.
机译：在工业过程中，通常同​​时监视几个相关的质量变量。多数多元质量控制图可有效地基于多元制造过程中的总体统计信息来检测失控信号。这样的图表的主要问题在于，它们可以检测到失控事件，但不能直接确定哪个变量或变量组导致了失控信号以及失控的幅度是多少。这项研究提出了一个基于人工神经网络的模型来补充多元χ2图表。此方法包含两个模块。在使用通用神经网络的第一个模块中，可以识别出非自然模式的类型。然后，通过使用两个特殊的神经网络分别计算移动平均值和趋势，可以同时识别每个变量的平均移动幅度和趋势斜率。所提出的方法的性能已使用模拟示例进行了评估。结果证实了所提出的方法提供了极好的分类率，并且训练过的网络所生成的输出与每种质量特性的相应实际目标值高度相关。

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