Utilizing Hybrid Deep Reinforcement Learning and Transformer Networks for Predictive Maintenance in Product Safety Engineering

Predictive maintenance (PdM) is essential to make sure the protection and reliability of the technology. This paper affords a remarkable technique that mixes hybrid deep reinforcement getting to know (DRL) with transformer networks to enhance predictive protection strategies for protection in material safety engineering. In most cases, inefficient and costly implementation strategies are based on predefined guidelines or ad hoc practices in traditional management. A system that integrates DRL and Transformer Networks to quickly identify and predict operational needs and respond effectively to various conditions. Hybrid DRL approach has the capability of non-modeling and reinforcing model-based learning for maintenance a it is best through communication with the system. enabling the study of systems Environmental and historical data. The recognized efficiency of transformer networks in sequential data processing will enhances the ability of the model to process and interpret the time series sensor data, helps to improves the prediction performanceThis method uses dual stage architecture which extract the time based features from sensor data. This approach agrees real-time optimization of emerging patterns and anomalies, This will improves prediction capabilities and reduces false positives. This model was tested on various industrial datasets, indicating significant developments in prediction accuracy and maintenance scheduling efficiency compared to traditional methods. By integrating these advanced techniques, The approach provides a more vigorous and adaptive solution for ensuring product safety and reliability in complex industrial environments. Proposed model achieved a notable reduction in Mean Absolute Error (MAE) 0.15 compared to traditional predictive maintenance methods.