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Fault Identification in Industrial Processes Using an Integrated Approach of Neural Network and Analysis of Variance

机译：使用神经网络和方差分析的集成方法识别工业过程中的故障

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摘要

Due to its importance in process improvement, the issue of determining exactly when faults occur has attracted considerable attention in recent years. Most related studies have focused on the use of the maximum likelihood estimator (MLE) method to determine the fault in univariate processes, in which the underlying process distribution should be known in advance. In addition, most studies have been devoted to identifying the faults of process mean shifts. Different from most of the current research, the present study proposes an effective approach to identify the faults of variance shifts in a multivariate process. The proposed mechanism comprises the analysis of variance (ANOVA) approach, a neural network (NN) classifier, and an identification strategy. To demonstrate the effectiveness of our proposed approach, a series of simulated experiments is conducted, and the best results from our proposed approach are addressed.
机译：由于其在过程改进中的重要性，因此准确确定何时发生故障的问题近年来引起了相当大的关注。大多数相关研究集中于使用最大似然估计器（MLE）方法确定单变量过程中的故障，在这种情况下应预先知道潜在的过程分布。此外，大多数研究都致力于识别过程均值偏移的缺陷。与当前的大多数研究不同，本研究提出了一种有效的方法来识别多变量过程中方差转移的缺陷。提出的机制包括方差分析（ANOVA）方法，神经网络（NN）分类器和识别策略。为了证明我们提出的方法的有效性，我们进行了一系列的模拟实验，并讨论了我们提出的方法的最佳结果。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第7期|516760.1-516760.7|共7页
  • 作者

    Yuehjen E. Shao; Chia-Ding Hou;

  • 作者单位

    Department of Statistics and Information Science, Fu Jen Catholic University, 510 Chungcheng Road, Xinzhuang District,New Taipei City 24205, Taiwan;

    Department of Statistics and Information Science, Fu Jen Catholic University, 510 Chungcheng Road, Xinzhuang District,New Taipei City 24205, Taiwan;

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