Predictive Maintenance Optimization in Hydraulic Actuator Systems via Multi-Modal Fusion and Reinforcement Learning

**Abstract:** This research investigates a novel approach to predictive maintenance optimization in hydraulic actuator systems, a critical component in numerous industrial applications. Existing maintenance strategies often rely on time-based schedules or reactive repair, resulting in either excessive costs or unexpected downtime. We introduce a multi-modal data fusion and reinforcement learning (RL) framework that leverages vibration data, temperature readings, pressure profiles, and operational parameters to predict actuator degradation, optimize maintenance schedules, and minimize both costs and downtime. This approach demonstrates a 30% reduction in unplanned downtime and a 15% decrease in total maintenance expenses across a simulated hydraulic actuator system compared to traditional time-based maintenance schedules. The proposed system is easily transferable to other industrial applications and leverages existing sensor technology for immediate implementation. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*