(255e) A Scalable Statistical Machine Learning Method: Application for Fault Detection and Fault Propagation Pattern Inference in the Tennessee Eastman Process
In this paper, we present a study on the scalability of the JPD estimation method. In particular, we apply the method to the large-scale Tennessee Eastman (TE) process [3, 4]. This process has a total of 91 variables (12 manipulated, 38 state, and 41 measured variables). It has five unit operations (a two-phase reactor, a condenser, a flash separator, a recycle compressor, and a product stripper). We show that the RP method is easily scalable, is computationally efficient and flexible, and allows for reliably estimating JPSs of large-scale highly nonlinear processes such as the TE process. Also, it is demonstrated that the RP method provides a computationally efficient and flexible framework for performing probabilistic inference in highly nonlinear systems with non-monotonic variable interdependencies. The advantages of this inference framework over Bayesian networks are presented.
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