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Aggregation Strategy on Federated Machine Learning Algorithm for Collaborative Predictive Maintenance.

Ali Bemani1, Niclas Björsell1

  • 1Department of Electrical Engineering, Mathematics and Science, University of Gävle, 80176 Gävle, Sweden.

Sensors (Basel, Switzerland)
|August 26, 2022
PubMed
Summary

Federated learning with FedSVM and FedLSTM enhances predictive maintenance by enabling collaborative model training at the edge without compromising data privacy. This approach improves accuracy and efficiency for industrial asset management.

Keywords:
aggregation strategydistributed machine learning algorithmedge and fog computingfederated learningresource allocation

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Area of Science:

  • Industrial IoT and Industry 4.0
  • Machine Learning and Artificial Intelligence
  • Data Science and Analytics

Background:

  • Industry 4.0 generates vast data from connected industrial assets, crucial for process optimization, quality control, and predictive maintenance (PM).
  • Traditional PM relies on centralized machine learning (ML) models, but transmitting large datasets from edge devices to the cloud incurs high costs, latency, and privacy risks.
  • Edge computing and federated learning (FL) offer solutions to process data locally, reducing transmission burdens and enhancing privacy.

Purpose of the Study:

  • To explore distributed machine learning for predictive maintenance (PM) applications in Industry 4.0 environments.
  • To propose novel federated learning algorithms, Federated Support Vector Machine (FedSVM) and Federated Long-Short Term Memory (FedLSTM), for enhanced PM.
  • To enable factories to improve PM model accuracy collaboratively without compromising data privacy.

Main Methods:

  • Development of two federated algorithms: FedSVM for anomaly detection and FedLSTM for Remaining Useful Life (RUL) estimation.
  • Implementation of a global model at the cloud level aggregating insights from fog-level federated models.
  • Evaluation using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset for engine RUL prediction.

Main Results:

  • FedSVM and FedLSTM demonstrated superior model accuracy compared to traditional methods.
  • The proposed federated approach achieved faster model convergence times.
  • Significant reduction in network usage resources was observed, highlighting efficiency gains.

Conclusions:

  • Federated learning, specifically FedSVM and FedLSTM, provides an effective and privacy-preserving solution for distributed predictive maintenance in Industry 4.0.
  • The algorithms enable collaborative model improvement across multiple factories without centralizing sensitive data.
  • This approach optimizes asset management by enhancing PM accuracy, reducing latency, and minimizing data transmission costs.