Toward a better monitoring statistic for profile monitoring via variational autoencoders
Toward a better monitoring statistic for profile monitoring via variational autoencoders
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DOI：
10.1080/00224065.2021.1903821
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发表时间：
2019-11
影响因子：
2.5
通讯作者：
N. Sergin;Hao Yan
中科院分区：
文献类型：
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作者：
N. Sergin;Hao Yan
摘要变分自编码器是近年来提出的一种用于过程监控的编码器。虽然这些工作显示出令人印象深刻的结果，在经典的方法，建议的监测统计往往忽略了学习的低维表示和计算的限制，在高维近似的不一致。在这项工作中，我们首先表现出这些问题，然后用一种新的统计公式来克服它们，该公式在不影响计算效率的情况下提高了失控检测的准确性。我们展示了我们的结果与显式控制潜在的变化，和现实生活中的例子，从热轧过程中获得的图像轮廓的模拟研究。
Abstract Variational autoencoders have been recently proposed for the problem of process monitoring. While these works show impressive results over classical methods, the proposed monitoring statistics often ignore the inconsistencies in learned lower-dimensional representations and computational limitations in high-dimensional approximations. In this work, we first manifest these issues and then overcome them with a novel statistic formulation that increases out-of-control detection accuracy without compromising computational efficiency. We demonstrate our results on a simulation study with explicit control over latent variations, and a real-life example of image profiles obtained from a hot steel rolling process.