Deep Learning-Based Fault Detection in Industrial Electronic Systems: A Predictive Maintenance Approach

Abstract

In recent years, industrial systems have experienced a paradigm shift with the introduction of Artificial Intelligence (AI) and Deep Learning (DL) techniques for predictive maintenance. Traditional fault detection approaches, relying on threshold-based monitoring and manual inspections, often fail to identify early-stage anomalies, leading to unplanned downtimes and financial losses. This study investigates the role of deep learning algorithms—particularly Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Autoencoders—in fault detection and predictive maintenance of industrial electronic systems. The paper analyzes sensor-based datasets from various industrial environments to evaluate model performance. Results show that deep learning models can detect potential faults with up to 98% accuracy, enabling early intervention and reduced maintenance costs. The findings highlight the transformative impact of deep learning in achieving reliability, operational efficiency, and sustainability in modern industrial settings.