Failure prediction in industrial systems constitutes a fundamental component for optimizing maintenance strategies, reducing operational costs, and ensuring safety within increasingly complex production environments. Conventional monitoring approaches, typically based on fixed thresholds or simplified statistical analyses, are often inadequate to capture the nonlinear, dynamic, and multi-scale behaviors that characterize modern industrial processes. This study presents a comprehensive and critical comparative analysis of the principal intelligent algorithms, including machine learning, deep learning, and hybrid approaches, applied to industrial failure prediction. By systematically evaluating their respective strengths, limitations, and domains of applicability, the study highlights persistent challenges, particularly regarding […] Read more at https://mjcellpress.com/article/mjes13/