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Cao, Liang

University of British Columbia

Soft sensor technology is an effective way to measure parameters that are hard to measure in real time. It is of significant importance for the monitoring, control, and optimization of industrial production processes. With the richness of process data and the rapid development of machine learning techniques, data-driven soft sensor technologies are increasingly favored. Although soft sensor models have great potential and value in industrial applications, they still face significant challenges, particularly in the areas of model interpretability and stability. Ensuring interpretability and stability is crucial because it directly impacts the reliability and safety of operations in hazardous industrial environments. This dissertation provides a detailed exploration of soft sensor technologies, focusing on enhancing their interpretability and stability for industrial process monitoring. Chapters 2 and 3 focus on improving the interpretability of soft sensors. Chapter 2 introduces the Extra Trees (ET) algorithm and employs SHapley Additive exPlanations (SHAP) to enhance the interpretability of this inherently accurate but complex model. Chapter 3 explores interpretable feature selection techniques, particularly emphasizing the role of SHAP in the selection of meaningful features from complex industrial data. Subsequently, we utilize the selected interpretable features to establish a simple soft sensor model. In Chapter 4, the main topic shifts to the stability of the soft sensor model; we propose a stable learning algorithm based on the generation of virtual samples to improve stability in the face of industrial disturbances and data scarcity. Chapter 5 delves into the role of causality in soft sensor modeling, demonstrating how mining causal relationships between variables can significantly improve both stability and interpretability. It also emphasizes the importance of incorporating the knowledge of the process to ensure precision in the discovery of causal relationships. Chapter 6 presents two methods for extracting unsupervised and supervised latent causal features. By extracting latent causal features, not only is interpretability retained, but our model also becomes more robust. Finally, we analyze the main contributions and consider how they can be utilized in industrial contexts to improve the efficiency, safety,reliability, and interpretability of soft sensors.

Text

2024-03-15

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/87586

Chemical and Biological Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/