Improved Machine Learning for Multiclass Fault Classification in Industrial Processes

Improved Machine Learning for Multiclass Fault Classification in Industrial Processes

<p dir="ltr">Multiclass fault classification in complex processes is challenging due to many classes, nonlinear dynamics, overlapping fault signatures, and expanding fault taxonomies. Traditional machine learning models often struggle in such settings. The goal of this paper is to de...

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Bibliographic Details
Main Author: Khaled Dhibi (16891524) (author)
Other Authors: Radhia Fezai (16869888) (author), Nour Basha (21385547) (author), Gasim Ibrahim (17032299) (author), Hanif Ahmed Choudhury (23739939) (author), Mohamed Sufiyan Challiwala (23739942) (author), Byanne Malluhi (22963447) (author), Hazem Nounou (16869900) (author), Nimir Elbashir (5244551) (author), Mohamed Nounou (3489386) (author)
Published: 2025
Subjects:
Information and computing sciences
Artificial intelligence
Data management and data science
Machine learning
Mathematical sciences
Statistics
Anomaly detection
Bayesian optimization
binary decomposition
condition monitoring
fault diagnosis
feature selection
interval-valued data
machine learning
multiclass classification
process monitoring
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