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A Novel Process Monitoring Method Based on Dynamic Related ReliefF-SFA Method

机译：一种基于动态相关收益-SFA方法的新型过程监测方法

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摘要

This paper proposes a condition monitoring method based on Dynamic Related ReliefF-SFA (DRRSFA), which solves the problem of huge variable dimensions and strong autocorrelation in process monitoring. First, the samples are mapped into a new space, and the slow projection components (SPCs) of the samples are calculated. The SPCs not only considers the autocorrelation between the variables, but also characterizes the inherent properties of the whole system. Second, the feature selection algorithm ReliefF is used to select the more weighted features, which is called the principal components(PCs) to achieve dimensionality reduction. Then the corresponding statistics and control limits are calculated based on the obtained PCs. Finally, the process monitoring using the proposed algorithm is performed by testing a numerical example and the actual production process data, and the results show the effectiveness of the proposed method.
机译：本文提出了一种基于动态相关收益-SFA（DRRSFA）的条件监测方法，解决了过程监测中巨大变量尺寸和强大自相关的问题。首先，将样品映射到新的空间中，并且计算样本的慢投影组件（SPC）。 SPC不仅考虑变量之间的自相关，而且还表征了整个系统的固有属性。其次，特征选择算法Relieff用于选择更加权的特征，该特征被称为主要组件（PC）以实现维数减少。然后基于所获得的PC计算相应的统计和控制限制。最后，通过测试数值示例和实际生产过程数据来执行使用所提出的算法的过程监控，结果表明了所提出的方法的有效性。

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