Adaptive machine learning models for predictive maintenance in industrial internet of things (IIoT) systems

Area of Science:

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

Background:

• Traditional fault prediction models in IIoT struggle with dynamic environmental and equipment changes.
• Enhanced accuracy and reliability are crucial for effective predictive maintenance in industrial settings.

Purpose of the Study:

• To evaluate the performance of adaptive Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) models for IIoT predictive maintenance.
• To compare adaptive models against non-adaptive models using metrics like accuracy, precision, recall, F1 score, and AUC-ROC.

Main Methods:

• Utilized adaptive machine learning models, including RL and DRL, for fault prediction in IIoT environments.
• Compared the performance of adaptive models against non-adaptive baselines (e.g., Random Forest, SVM).
• Integrated adaptive models with edge and cloud computing for rapid decision-making and system integration.

Main Results:

• Adaptive models demonstrated superior performance in fault prediction compared to traditional models.
• Adaptive models achieved higher accuracy, precision, and recall, significantly reducing false positives and negatives.
• The Adaptive Ensemble model achieved 93.4% accuracy and 95.2% AUC-ROC, outperforming non-adaptive baselines.

Conclusions:

• Adaptive learning models enhance the accuracy and reliability of predictive maintenance systems in IIoT.
• Adaptive models effectively handle environmental and equipment variability, offering significant improvements over non-adaptive approaches.
• The study provides valuable insights for industries seeking to optimize their PdM systems for efficiency and cost-effectiveness.