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The integration of Industrial IoT (IIoT) has revolutionized maintenance practices by enabling extensive data collection from machinery, fostering predictive maintenance strategies to minimize downtime and reduce costs. Estimating the Remaining Useful Life (RUL) of components is crucial for timely proactive maintenance. Edge computing enhances these processes by addressing latency and bandwidth challenges through real-time data processing close to the source. In this work, we explore the use of edge-based machine learning algorithms in IIoT environments to provide real-time insights and predictions about equipment health. We examine edge computing frameworks for their scalability, reliability, and security. Case studies and experimental results demonstrate the effectiveness of edge intelligence in optimizing predictive maintenance. Our findings highlight the potential of edge intelligence in industrial maintenance, providing valuable insights for researchers and practitioners. We present an analysis of edge computing applications for predictive maintenance and compare the performance of different federated learning aggregation methods against a centralized model. Results show that federated learning offers competitive performance while maintaining data privacy.

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Edge-Based Federated Learning Methods for Remaining Useful Life Estimation in IIoT

  • 会议论文

  • 发表日期：

    2025年

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  • Dimitrios Amaxilatis,
  • Sarantis Papachristofilou,
  • Christos Zaroliagis

摘要

The integration of Industrial IoT (IIoT) has revolutionized maintenance practices by enabling extensive data collection from machinery, fostering predictive maintenance strategies to minimize downtime and reduce costs. Estimating the Remaining Useful Life (RUL) of components is crucial for timely proactive maintenance. Edge computing enhances these processes by addressing latency and bandwidth challenges through real-time data processing close to the source. In this work, we explore the use of edge-based machine learning algorithms in IIoT environments to provide real-time insights and predictions about equipment health. We examine edge computing frameworks for their scalability, reliability, and security. Case studies and experimental results demonstrate the effectiveness of edge intelligence in optimizing predictive maintenance. Our findings highlight the potential of edge intelligence in industrial maintenance, providing valuable insights for researchers and practitioners. We present an analysis of edge computing applications for predictive maintenance and compare the performance of different federated learning aggregation methods against a centralized model. Results show that federated learning offers competitive performance while maintaining data privacy.