Reshaping Industrial Maintenance with Machine Learning: Fouling Control Using Optimized Gaussian Process Regression

Reliable maintenance scheduling is essential for complex
industrial
equipment, yet traditional condition-based strategies with static
warning thresholds often fail in fouling-prone processes or when feedstock
composition fluctuates. This paper presents a predictive maintenance
strategy based on the automatic selection of optimal kernel combinations
in Gaussian Process Regression (GPR) through a recursive algorithm.
The approach is applied to a vacuum distillation column processing
used oil, a fouling-prone waste stream with variable composition.
The algorithm performs an automated search and optimization of models
through recursive combination of kernels and operators, following
a greedy search strategy. The algorithm’s predictive capabilities
are validated on five distinct data sets representing the evolution
of the column’s pressure differential, a key indicator of fouling.
Results show significant improvements, with the strategy reducing
suboptimal operating time by 30–40% and, in some cases, entirely
avoiding such conditions. Automation of kernel search and optimization
ensures general validity for the proposed method.