Optimized predictive maintenance for streaming data in industrial IoT networks using deep reinforcement learning and...

Area of Science:

• Industrial Internet of Things (IIoT)
• Machine Learning
• Predictive Maintenance

Background:

• Industrial IoT (IIoT) networks face significant challenges in predictive maintenance due to dynamic conditions, device heterogeneity, and evolving data patterns.
• Existing methods often struggle with real-time adaptation and robust fault classification in complex IIoT environments.

Purpose of the Study:

• To introduce an ensemble-based framework integrating Deep Reinforcement Learning (DRL), Random Forest (RF), and Gradient Boosting Machines (GBM) for enhanced fault prediction and maintenance efficiency in IIoT.
• To address the limitations of dynamic conditions, device heterogeneity, and data pattern evolution in IIoT predictive maintenance.

Main Methods:

• Utilized Deep Reinforcement Learning (DRL) for adaptive fault prediction and dynamic learning from real-time sensor data.
• Employed Random Forest (RF) for robust fault classification, specifically addressing class imbalance issues common in IIoT data.
• Integrated Gradient Boosting Machines (GBM) to leverage feature dependencies for improved predictive accuracy and generalization.

Main Results:

• The ensemble framework demonstrated superior performance in fault prediction and maintenance efficiency compared to traditional methods.
• Achieved significant improvements in accuracy, precision, recall, and F1-score, while minimizing latency and false positives.
• Validated through extensive simulations, showing enhanced fault detection reliability and dynamic adaptation capabilities.

Conclusions:

• The proposed ensemble framework offers a scalable and adaptive predictive maintenance strategy for IIoT systems.
• Successfully improves operational efficiency, reduces unplanned downtime, and lowers costs in industrial settings.
• The integration of DRL, RF, and GBM provides a robust solution for complex IIoT predictive maintenance challenges.