Quality-Driven Autoencoder for Nonlinear Quality-Related
and Process-Related Fault Detection Based on Least-Squares Regularization
and Enhanced Statistics
Version 2 2020-06-17, 12:04
Version 1 2020-06-17, 10:13
Posted on 2020-06-17 - 12:04
Although many kernel-based quality-related monitoring methods have
been developed for nonlinear processes, the nonlinearity between process
variables and quality indicators is not well interpreted by kernel
mapping and subsequent regression. To monitor a nonlinear quality-related
latent space, a novel framework that consists of quality-related and
process-related statistics rather than quality-related and quality-independent
statistics is proposed. First, we train a quality-driven autoencoder
(QdAE) with least-squares regularization through the gradient descent
algorithm using quality indicators. Quality-related information can
be predicted using latent variables through the auxiliary supervision
of the quality indicators. Second, quality-related statistic Ty2 is constructed to monitor the quality indicators.
In the residual subspace derived by the QdAE, we compute the SPE statistic, which contains quality-related and quality-independent
information. Furthermore, we present a strategy to enhance the SPE statistic to improve performance. Considering the quality-related
and process-related monitoring using Ty2 and SPEnew, we can
also provide a reliable decision about whether the fault is quality-related
or quality-independent. Finally, the proposed method is evaluated
using the cases in the Tennessee Eastman process.
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Yan, Shifu; Yan, Xuefeng (2020). Quality-Driven Autoencoder for Nonlinear Quality-Related
and Process-Related Fault Detection Based on Least-Squares Regularization
and Enhanced Statistics. ACS Publications. Collection. https://doi.org/10.1021/acs.iecr.0c00735
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