Modern power system optimization problems have become increasingly large-scale, non-convex, and highly constrained. Conventional optimization methods and metaheuristics struggle with these complexities, primarily due to premature convergence, extensive parameter-tuning requirements, and the limitations of the No Free L...
Efficient operation of interconnected production systems requires accurate modeling of upstream–downstream interactions to support informed operational decision-making. This study proposes an interconnected Adaptive Neuro-Fuzzy Inference System (ANFIS) framework consisting of two sequentially linked ANFIS models repres...
Paraskevi Zacharia, Konstantinos Sotiropoulos, Constantinos I. Stergiou· Electronics· 0 citations
Industrial fault detection and diagnosis are essential for maintaining operational reliability and minimizing performance degradation in process industries. This study presents an integrated machine learning framework for fault detection, fault-type classification, and fault severity estimation using a synthetic chemic...
Paraskevi Zacharia, Styliani Kontaki, K. Moustris et al.· Machines· 0 citations
Industrial production systems require intelligent maintenance solutions to minimize unplanned downtime, improve reliability, and support data-driven decision making. This study presents a machine learning framework for condition monitoring and fault classification in industrial production systems. The framework is eval...
Paraskevi Zacharia, Konstantinos Botsis, K. Moustris et al.· Machines· 0 citations
Results demonstrate that CNN-based architectures achieve robust performance in capturing local spatial features, while transformer-based models provide competitive results through global contextual representation, however, variations are observed in model calibration and false-negative rates, which are critical for cli...
