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Related Experiment Video

Updated: Jan 7, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

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Artificial Intelligence of Things for Next-Generation Predictive Maintenance.

Taimia Bitam1, Aya Yahiaoui1, Djallel Eddine Boubiche1

  • 1LEREESI Laboratory, HNS-RE2SD, Batna 05000, Algeria.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary
This summary is machine-generated.

Industry 5.0 enhances industrial ecosystems with human-centric approaches. AIoT-enabled Predictive Maintenance (PdM) is crucial for advanced fault detection and system resilience.

Keywords:
Artificial Intelligence of ThingsIndustry 5.0predictive maintenancesmart manufacturing

Related Experiment Videos

Last Updated: Jan 7, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

362

Area of Science:

  • Industrial Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Industry 5.0 prioritizes human-centric, sustainable, and resilient industrial systems.
  • Traditional maintenance methods struggle with complex, data-driven environments.
  • Predictive Maintenance (PdM) is vital for Industry 5.0's adaptive operations.

Purpose of the Study:

  • To systematically review AIoT-enabled PdM in the context of Industry 5.0.
  • To provide a unified taxonomy of AI models, IIoT infrastructures, and AIoT architectures.
  • To analyze AI-driven techniques and sector-specific applications.

Main Methods:

  • Systematic literature review of AIoT-enabled PdM.
  • Development of a unified taxonomy for AI models, IIoT, and AIoT architectures.
  • Analysis of AI techniques, sector implementations, and emerging paradigms.

Main Results:

  • AIoT enables real-time sensing, learning, and decision-making for PdM.
  • Key AI techniques include fault detection, Remaining Useful Life (RUL) estimation, and prescriptive actions.
  • Emerging paradigms like Edge-Cloud, federated learning, and digital twins enhance autonomous maintenance.

Conclusions:

  • AIoT-based PdM is foundational for Industry 5.0's goals.
  • Future research should focus on explainability, data quality, cybersecurity, and human-AI collaboration.
  • This review provides a framework for developing scalable and trustworthy AIoT-PdM systems.