Open Access
ARTICLE
Deep Learning-Driven Fault Detection in Industrial Automation: Enhancing Reliability and Predictive Maintenance Efficiency
Issue Vol. 1 No. 01 (2025): Volume 01 , Issue 01 Oct 2025 --- Section Articles --- Published Date: 2025-10-01
Abstract
Industrial automation systems are becoming increasingly complex due to the integration of robotics, advanced sensors, distributed control systems, cyber–physical architectures, and interconnected industrial IoT platforms. As production becomes more digitized, the risk of equipment faults, system failures, and operational downtime also increases. Traditional rule-based fault detection systems often fail to capture hidden anomalies in multi-dimensional data and lack adaptability to real-time variations. This research examines how deep learning-driven fault detection—using neural networks, convolutional models, recurrent architectures, and hybrid deep learning pipelines—significantly improves fault classification accuracy, early anomaly detection, and predictive maintenance efficiency. The paper assesses multiple industrial use cases including manufacturing Assembly Lines, HVAC units, automated packaging machines, and predictive machinery failure. A comprehensive methodology, real-world case study, two data tables, and an original field-level questionnaire deepen the analysis. Results show that deep learning models outperform traditional approaches by 20–45% in accuracy and reduce unplanned downtime by up to 60%. The findings highlight the critical role of AI-enabled predictive maintenance in improving industrial reliability and long-term asset sustainability.
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