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1. A Self-Interpretable Soft Sensor Based
on Deep Learning and Multiple Attention
Mechanism: From Data Selection to
Sensor Modeling
Abstract
For deep learning-based soft sensors, the lack of interpretability and the
consequent unreliability has become one of the most important problems. In
this article, a neural network scheme called the deep multiple attention soft
sensor (DMASS), which consis
to develop a self-interpretable soft sensor. DMASS was established to ensure
the self-interpretability of data selection and sensor modeling and try to
integrate these originally independent phases into the si
the existing attention mechanisms’ core implementation steps are
summarized as a unified form, and then the variable attention mechanism and
time lag attention mechanism are proposed. When DMASS's training is
completed, the obtained att
Interpretable Soft Sensor Based
on Deep Learning and Multiple Attention
Mechanism: From Data Selection to
Sensor Modeling
based soft sensors, the lack of interpretability and the
consequent unreliability has become one of the most important problems. In
this article, a neural network scheme called the deep multiple attention soft
sensor (DMASS), which consists solely of attention mechanisms, is proposed
interpretable soft sensor. DMASS was established to ensure
interpretability of data selection and sensor modeling and try to
integrate these originally independent phases into the single scheme. First,
the existing attention mechanisms’ core implementation steps are
summarized as a unified form, and then the variable attention mechanism and
time lag attention mechanism are proposed. When DMASS's training is
completed, the obtained attention weights provide the self-interpretable data
Interpretable Soft Sensor Based
on Deep Learning and Multiple Attention
Mechanism: From Data Selection to
based soft sensors, the lack of interpretability and the
consequent unreliability has become one of the most important problems. In
this article, a neural network scheme called the deep multiple attention soft
ts solely of attention mechanisms, is proposed
interpretable soft sensor. DMASS was established to ensure
interpretability of data selection and sensor modeling and try to
ngle scheme. First,
the existing attention mechanisms’ core implementation steps are
summarized as a unified form, and then the variable attention mechanism and
time lag attention mechanism are proposed. When DMASS's training is
interpretable data

2. selection results. Then, a self-attention activation structure (SAAS) is
proposed to extract the nonlinear spatio-temporal features of data. The
mathematical expression for the extracted feature, the SAAS's attention
matrix, the information path diagram for DMASS's training, and the
uncertainty-aware interval prediction show the self-interpretability of sensor
modeling. Finally, DMASS was applied to predict the thermal deformation of
the air preheater rotor, and the validity of DMASS's self-interpretability is
verified by the known mechanism analysis and information bottleneck theory.
Meanwhile, DMASS's great sensing performance was confirmed through
comparison with other novel soft sensors.