AI-Driven Predictive Maintenance for Industrial Engineering Systems

Eszter Horváth

AI-Driven Predictive Maintenance for Industrial Engineering Systems

Author(s)	Eszter Horváth
Country	Hungary
Abstract	Industrial engineering systems depend on the reliable operation of machines, production equipment, transportation mechanisms, energy systems, and automated manufacturing infrastructure. Unexpected equipment failures can interrupt production, increase maintenance costs, reduce asset utilization, and create safety risks. Conventional maintenance approaches based on fixed schedules or reactive intervention are often unable to accurately reflect the changing condition of industrial assets. Artificial intelligence provides new opportunities for predictive maintenance by enabling industrial systems to analyze large volumes of sensor-generated data and identify patterns associated with equipment degradation. This paper examines AI-driven predictive maintenance as an intelligent approach for monitoring and managing industrial engineering systems. The study focuses on sensor-based data acquisition, data preprocessing, feature engineering, machine learning, deep learning, anomaly detection, remaining useful life estimation, and maintenance decision support. A conceptual framework is developed in which industrial equipment continuously generates operational data that are processed through edge or centralized analytical platforms. AI models then identify abnormal patterns and estimate potential failure conditions. The analysis indicates that AI-driven predictive maintenance can improve equipment condition visibility, support earlier fault detection, and enhance maintenance planning. The integration of multiple sensor modalities can further improve the representation of complex equipment conditions, while edge-based AI can reduce latency for time-sensitive industrial applications. However, practical deployment remains constrained by data quality, insufficient failure examples, model interpretability, cybersecurity, computational requirements, sensor reliability, and differences among industrial assets. Future research should focus on explainable AI, transfer learning, federated learning, digital twins, adaptive models, and human-centered maintenance decision systems. AI-driven predictive maintenance therefore represents a significant pathway toward more reliable, efficient, adaptive, and intelligent industrial engineering environments.
Keywords	Artificial Intelligence, Predictive Maintenance, Industrial Engineering, Machine Learning, Deep Learning, Condition Monitoring, Fault Detection, Remaining Useful Life
Field	Engineering
Published In	Volume 2, Issue 7, July 2025
Published On	2025-07-15

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AI-Driven Predictive Maintenance for Industrial Engineering Systems

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