AI-Driven Predictive Maintenance for U.S. Smart Manufacturing: Deep Learning Models for Equipment Failure Prediction and Operational Resilience

Abstract

The increasing digitalization of manufacturing systems has intensified the need for intelligent maintenance strategies capable of minimizing downtime and enhancing operational resilience. This study develops an AI-driven predictive maintenance framework for U.S. smart manufacturing environments by integrating deep learning models, survival analysis, and multi-objective optimization techniques. The proposed framework leverages IoT-enabled sensor data to identify equipment degradation patterns, predict failure probabilities, and estimate remaining useful life with high accuracy. Beyond failure prediction, the framework generates optimized maintenance schedules that balance maintenance costs, production continuity, resource utilization, and operational reliability. Empirical validation across diverse manufacturing sectors demonstrates significant improvements in predictive performance, reduced unplanned downtime, lower maintenance expenditures, and enhanced system resilience. The findings highlight the effectiveness of combining advanced artificial intelligence techniques with resilience-oriented maintenance planning to support data-driven decision-making in Industry 4.0 environments. This research contributes a scalable and practical framework that strengthens manufacturing competitiveness, sustainability, and long-term operational performance.

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