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

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...

Full description

Saved in:
Bibliographic Details
Main Author: Francesco Negri (12992994) (author)
Other Authors: Andrea Galeazzi (11632389) (author), Francesco Gallo (3428087) (author), Flavio Manenti (1718206) (author)
Published: 2025
Subjects:
Medicine
Sociology
Space Science
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
Information Systems not elsewhere classified
yet traditional condition
reshaping industrial maintenance
gaussian process regression
complex industrial equipment
30 – 40
prone waste stream
optimal kernel combinations
feedstock composition fluctuates
greedy search strategy
kernel search
variable composition
prone processes
automated search
recursive combination
recursive algorithm
proposed method
pressure differential
predictive capabilities
paper presents
machine learning
key indicator
entirely avoiding
column ’
based strategies
automatic selection
algorithm ’
algorithm performs
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items