<![CDATA[In the era of Industry 4.0, the integration of advanced technologies like the Internet of Things (IoT) into risk-based maintenance planning systems has become crucial for optimizing operational efficiency. This research explores methods to enhance maintenance decision-making by integrating real-time IoT data with risk-based maintenance models. Traditional risk-based maintenance often relies on historical data, which can be insufficient for responding to dynamic operational conditions. By leveraging IoT's ability to collect continuous, real-time data, this study aims to improve the accuracy and responsiveness of maintenance strategies. The research employs a systematic methodology, including data collection through IoT sensors, data preprocessing, and the development of predictive models using machine learning techniques such as Random Forest and Neural Networks. The results indicate that IoT integration reduces downtime by predicting equipment failures with higher accuracy, leading to a 30% reduction in maintenance costs and a 25% increase in productivity. This study demonstrates the significant potential of IoT in transforming maintenance strategies from reactive to proactive, ultimately enhancing equipment reliability and extending operational lifespan.]]>
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