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As global industries transition towards Industry 5.0, predictive maintenance (PM) remains crucial for cost-effective operations, resilience, and minimizing downtime in increasingly smart manufacturing environments. In this chapter, we explore how the integration of Federated Learning (FL) and blockchain (BC) technologies enhances the prediction of machinery’s Remaining Useful Life (RUL) within decentralized and human-centric industrial ecosystems. Traditional centralized data approaches raise concerns over privacy, security, and scalability, especially as Artificial Intelligent (AI)-driven smart manufacturing becomes more prevalent. This chapter leverages FL to enable localized model training across multiple sites while utilizing BC to ensure trust, transparency, and data integrity across the network. This BC-integrated FL framework optimizes RUL predictions, enhances data privacy and security, establishes transparency, and promotes collaboration in decentralized manufacturing. It addresses key challenges such as (1) maintaining privacy and security, (2) ensuring transparency and fairness, and (3) incentivizing participation in decentralized networks. Experimental validation using NASA’s CMAPSS dataset demonstrates the model’s effectiveness in real-world scenarios, and we extend our findings to the broader research community through open-source code on GitHub, inviting collaborative development to drive innovation in Industry 5.0.

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A New Framework for Prognostics in Decentralized Industries: Enhancing Fairness, Security, and Transparency Through Blockchain and Federated Learning

  • 章节

  • 发表日期：

    2026年

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  • T. Q. D. Pham,
  • K. D. Tran,
  • Khanh T. P. Nguyen,
  • X. V. Tran,
  • L. Köehl,
  • K. P. Tran

摘要

As global industries transition towards Industry 5.0, predictive maintenance (PM) remains crucial for cost-effective operations, resilience, and minimizing downtime in increasingly smart manufacturing environments. In this chapter, we explore how the integration of Federated Learning (FL) and blockchain (BC) technologies enhances the prediction of machinery’s Remaining Useful Life (RUL) within decentralized and human-centric industrial ecosystems. Traditional centralized data approaches raise concerns over privacy, security, and scalability, especially as Artificial Intelligent (AI)-driven smart manufacturing becomes more prevalent. This chapter leverages FL to enable localized model training across multiple sites while utilizing BC to ensure trust, transparency, and data integrity across the network. This BC-integrated FL framework optimizes RUL predictions, enhances data privacy and security, establishes transparency, and promotes collaboration in decentralized manufacturing. It addresses key challenges such as (1) maintaining privacy and security, (2) ensuring transparency and fairness, and (3) incentivizing participation in decentralized networks. Experimental validation using NASA’s CMAPSS dataset demonstrates the model’s effectiveness in real-world scenarios, and we extend our findings to the broader research community through open-source code on GitHub, inviting collaborative development to drive innovation in Industry 5.0.