AI-Driven Predictive Maintenance Optimization for U.S. Smart Manufacturing Systems

Predictive maintenance is becoming essential for modern U.S. manufacturing plants as unplanned machine downtime leads to significant productivity losses, supply delays, and increased operational costs. This research proposes an AI-driven predictive maintenance framework that integrates Industrial Internet of Things (IoT) sensor streams, machine learning failure prediction, and reliability-based maintenance scheduling. The model utilizes vibration, temperature, power consumption, and operational cycle data to detect early-stage degradation patterns in industrial equipment. A hybrid deep learning and survival analysis approach is introduced to estimate Remaining Useful Life (RUL) and predict the probability of failure over time. Additionally, an optimization layer is developed to automatically generate cost-effective maintenance schedules that minimize downtime while balancing labor availability and spare parts constraints. The proposed framework is highly scalable and can be implemented across diverse manufacturing sectors, including automotive, semiconductor, and aerospace production. By improving equipment reliability, reducing emergency repairs, and supporting Industry 4.0 modernization, this work directly contributes to U.S. manufacturing competitiveness and industrial resilience.