2023 Theses Doctoral
Machine Learning Framework for Causal Modeling for Process Fault Diagnosis and Mechanistic Explanation Generation
Machine learning models, typically deep learning models, often come at the cost of explainability. To generate explanations of such systems, models need to be rooted in first-principles, at least mechanistically. In this work we look at a gamete of machine learning models based on different levels of process knowledge for process fault diagnosis and generating mechanistic explanations of processes. In chapter 1, we introduce the thesis using a range of problems from causality, explainability, aiming towards the goal of generating mechanistic explanations of process systems. Chapter 2 looks at an approach for generating causal models purely through data-centric approach, with minimal process knowledge with respect to equipment connectivity and identifying causality in the domains. These causal models generated can be utilized for process fault diagnosis.
Chapter 3 and chapter 4 show how deep learning models can be used for both classification for process fault diagnosis and regression. We see that depending on the hyperparameters, i.e., purely the breadth and depth of a neural network, the learned hidden representations vary from a simple set of features, to more complex sets of features. While these hidden representations may be exploited to aid in classification and regression problems, the true explanations of these representations do not correlate with mechanisms in the system of interest. There is thus a requirement to add more mechanistic information about the features generated to aid in explainability.
Chapter 5 shows how incorporating process knowledge can aid in generating such mechanistic explanations based on automated variable transformations. In this chapter we show how process knowledge can be used to generate features, or model forms to generate explainable models. These models have the ability of extracting the true models of the system from the model knowledge provided.
- Sivaram_columbia_0054D_17588.pdf application/pdf 25.9 MB Download File
More About This Work
- Academic Units
- Chemical Engineering
- Thesis Advisors
- Venkatasubramanian, Venkat
- Ph.D., Columbia University
- Published Here
- December 7, 2022