Intelligent Fault Detection through Real-Time Data Analysis

https://doi.org/10.58571/CNCA.AMCA.2025.021

Resumen: This paper presents a comparative analysis of four strategies for intelligent fault classification based on real-time data analysis. The number of research publications proposing different intelligent classification strategies is extensive, but the vast majority of the proposed classifiers operate offline. This paper explores different intelligent classification techniques with the goal of implementing them in real-time for fault detection in nonlinear dynamic systems. Real-time data classification is highly relevant for the design of fault-tolerant control systems.

¿Cómo citar?

Alvarez, J., Sanchez, O. & Alanis, A. (2025). Intelligent Fault Detection through Real-Time Data Analysis. Memorias del Congreso Nacional de Control Automático 2025, pp. 121-125. https://doi.org/10.58571/CNCA.AMCA.2025.021

Palabras clave

Time series modeling; Fault detection and diagnosis; Artificial neural networks; Classification; Real-time systems.

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