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Toward a better monitoring statistic for profile monitoring via variational autoencoders
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Toward a better monitoring statistic for profile monitoring via variational autoencoders

Publication:
Journal of Quality Technology
Date:
November 2021
Issue:
Volume 53 Issue 5
Pages:
pp. 454-473
Author(s):
Sergin, Nurettin Dorukhan, Yan, Hao

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.

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