Bayesian-optimized Gaussian process-based fault classification in industrial processes

The integration of data-driven modeling techniques in machine learning applications, such as multiclass classification, has resulted in robust classifier designs. However, one of the main drawbacks of this approach has been the rising complexity of modeling as the number of classes in the system increases, which may eventually make the overall design of the classifier unfavorable regardless of the expected performance. In this paper, we will discuss the design of a novel logic-based Bayesian-optimized Gaussian process (BOGP) classifier that aims to minimize the number of independent empirical models needed to accurately diagnose multiple distinct fault classes in industrial process. Moreover, the fault classification accuracy of the BOGP classifier is compared to the respective performances of other methods published in literature, and the Tennessee Eastman process is used as a benchmark case study for all methods.