Nonlinear Non-Gaussian and Multimode Process Monitoring-Based Multi-Subspace Vine Copula and Deep Neural Network

This
paper proposes a robust copula
double-subspace (CDS) model
based on a sparse robust autoencoder (SRAE) named SRAE-CDS. By reconstructing
training data with SRAE, the resulting SRAE-CDS model is more robust
to changes in inputs and is more sensitive to process faults. A five-layer
SRAE model is proposed to classify the multimode process and extract
abstract features, which is first introduced in fault detection area.
The SRAE model not only classifies the operating conditions but also
extracts high-level features. According to the abstract features,
the CDS method, which is good at handling non-Gaussian and nonlinear
data, is used to depict the distribution of the advanced features.
To perform process monitoring, the highest density region index under
a given control limit is calculated in real time. This paper first
proposes the mode identification method with the SRAE model and monitors
the industrial process through depicting the distribution of the middle
layer in deep neural network, achieving good performance. The effectiveness
and benefits of the SRAE-CDS method are illustrated in three experiments:
the first is a numerical example, the second is a Tennessee Eastman
benchmark process for fault detection, and the third is a real ethylene
cracking furnace process. The results show that the SRAE-CDS model
achieves good performance for industrial processes.