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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Related Experiment Video

Updated: Sep 13, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Optimized predictive maintenance for streaming data in industrial IoT networks using deep reinforcement learning and

K Varalakshmi1, J Kumar2

  • 1School of Electronics Engineering, VIT-AP University, Inavolu, Amaravathi, 522 237, Andhra Pradesh, India.

Scientific Reports
|July 27, 2025
PubMed
Summary

This study presents an ensemble framework for Industrial IoT predictive maintenance, combining Deep Reinforcement Learning (DRL), Random Forest (RF), and Gradient Boosting Machines (GBM). It enhances fault prediction and maintenance efficiency in dynamic industrial environments.

Keywords:
DRLEnsemble MethodsIndustrial Internet of ThingsPredictive MaintenanceReal-Time Fault Prediction

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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.