In statistical process monitoring, statistical control tools are used to identify deviations from normal operating conditions. In many industrial processes, such as batch production processes, multiple process variables must be monitored as they play a key role in the quality of the final product. Several monitoring tools are found in the literature that deal with the case of multiple process variables, but a few of them deal with the case of autocorrelated data. Existing tools that are used to monitor autocorrelated process variables have two main drawbacks. First, they are run offline. That is, monitoring is performed at the end of the production cycle and hence no corrective actions can be made. Second, these tools utilize dimensionality reduction techniques to solve for computational complexity issues. A Gaussian Process–based modeling approach is proposed in this work to monitor batch production processes online. The proposed monitoring approach takes into consideration of both the correlation between process variables and the within variable autocorrelations. A simulated and two real data sets were used to evaluate the performance of the proposed modeling approach. Model performance metrics showed that the proposed modeling approach has similar performance to the optimal case of Shewhart type control charts. Process status classification accuracy of about 92–98% were achieved for the cases considered.

中文翻译：

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用于监控自相关批量生产过程的高斯过程方法

在统计过程监控中，统计控制工具用于识别与正常操作条件的偏差。在许多工业过程中，例如批量生产过程，必须监控多个过程变量，因为它们在最终产品的质量中起着关键作用。在处理多过程变量情况的文献中发现了几种监控工具，但其中一些处理自相关数据的情况。用于监控自相关过程变量的现有工具有两个主要缺点。首先，它们是离线运行的。也就是说，监控是在生产周期结束时进行的，因此无法采取纠正措施。其次，这些工具利用降维技术来解决计算复杂性问题。在这项工作中提出了一种基于高斯过程的建模方法来在线监控批量生产过程。建议的监控方法考虑了过程变量之间的相关性和变量内的自相关。使用一个模拟数据集和两个真实数据集来评估所提出的建模方法的性能。模型性能指标表明，所提出的建模方法与休哈特型控制图的最佳情况具有相似的性能。对于所考虑的案例，过程状态分类准确度达到了约 92-98%。使用一个模拟数据集和两个真实数据集来评估所提出的建模方法的性能。模型性能指标表明，所提出的建模方法与休哈特型控制图的最佳情况具有相似的性能。对于所考虑的案例，过程状态分类准确度达到了约 92-98%。使用一个模拟数据集和两个真实数据集来评估所提出的建模方法的性能。模型性能指标表明，所提出的建模方法与休哈特型控制图的最佳情况具有相似的性能。对于所考虑的案例，过程状态分类准确度达到了约 92-98%。