  The dynamic characteristics of multiphase industrial processes present
significant challenges in the field of industrial big data modeling.
Traditional soft sensing models frequently neglect the process dynamics and
have difficulty in capturing transient phenomena like phase transitions. To
address this issue, this article introduces a causality-driven sequence
segmentation (CDSS) model. This model first identifies the local dynamic
properties of the causal relationships between variables, which are also
referred to as causal mechanisms. It then segments the sequence into different
phases based on the sudden shifts in causal mechanisms that occur during phase
transitions. Additionally, a novel metric, similarity distance, is designed to
evaluate the temporal consistency of causal mechanisms, which includes both
causal similarity distance and stable similarity distance. The discovered
causal relationships in each phase are represented as a temporal causal graph
(TCG). Furthermore, a soft sensing model called temporal-causal graph
convolutional network (TC-GCN) is trained for each phase, by using the
time-extended data and the adjacency matrix of TCG. The numerical examples are
utilized to validate the proposed CDSS model, and the segmentation results
demonstrate that CDSS has excellent performance on segmenting both stable and
unstable multiphase series. Especially, it has higher accuracy in separating
non-stationary time series compared to other methods. The effectiveness of the
proposed CDSS model and the TC-GCN model is also verified through a penicillin
fermentation process. Experimental results indicate that the breakpoints
discovered by CDSS align well with the reaction mechanisms and TC-GCN
significantly has excellent predictive accuracy.
