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A hybrid Bi-LSTM model for data-driven maintenance planning.

Alexandros Noussis1, Ryan O'Neil1, Ahmed Saif1

  • 1Department of Industrial Engineering, Dalhousie University, Halifax, NS Canada.

Autonomous Intelligent Systems
|June 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid deep learning model for optimizing asset maintenance. The framework generates remaining useful life (RUL) predictions to improve selective maintenance planning and reduce costs in complex industrial systems.

Keywords:
Deep learningReliability and maintenance optimizationSelective maintenanceSystem prognostics

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Area of Science:

  • Engineering
  • Computer Science
  • Operations Research

Background:

  • Modern industries require efficient asset maintenance under resource constraints.
  • Classical maintenance methods face limitations due to estimation errors and computational complexity.
  • Industry 4.0 and deep learning (DL) enable data-driven health predictions for maintenance planning.

Purpose of the Study:

  • To bridge the gap between DL-based remaining useful life (RUL) predictions and maintenance plan optimization.
  • To develop a scalable and accurate framework for the selective maintenance problem (SMP).
  • To optimize maintenance for mission-oriented series k-out-of-n:G systems.

Main Methods:

  • Development of a hybrid DL model incorporating Monte Carlo dropout for RUL predictions.
  • Construction of empirical system reliability functions from RUL predictions.
  • Optimization of the selective maintenance problem (SMP) using the generated reliability functions.

Main Results:

  • The proposed framework effectively optimizes maintenance plans, minimizing costs while ensuring mission survival.
  • Numerical experiments demonstrate superior performance compared to prior SMP methods.
  • The method provides exact solutions without computationally intensive parametric reliability functions.

Conclusions:

  • The developed hybrid DL framework offers a scalable and accurate solution for complex industrial maintenance scenarios.
  • It enables data-driven, optimized maintenance planning, enhancing operational efficiency and reducing costs.
  • The approach is applicable across various industrial contexts and system configurations.