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Related Experiment Videos

A two-stage framework for cost-sensitive predictive maintenance using deep learning, GANs, and risk-aware clustering.

Ali Hakami1

  • 1Mechanical and Industrial Department, College of Engineering and Computing in Al-Gunfudha, Umm Al-Qura University, 21961, Mecca, Saudi Arabia. amahakami@uqu.edu.sa.

Scientific Reports
|March 22, 2026
PubMed
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This study introduces a novel predictive maintenance (PdM) approach using machine learning to optimize maintenance scheduling. The method enhances Remaining Useful Life (RUL) prediction and cost-aware decision-making, reducing failures and costs.

Area of Science:

  • Industrial Engineering
  • Machine Learning
  • Data Science

Background:

  • Predictive maintenance (PdM) faces challenges in data scarcity and cost-uncertainty.
  • Current PdM focuses on Remaining Useful Life (RUL) prediction, often neglecting maintenance scheduling optimization.
  • A component-based, decision-oriented approach is needed to integrate RUL with maintenance optimization.

Purpose of the Study:

  • To propose a novel two-stage PdM framework linking RUL prediction with maintenance optimization.
  • To address data scarcity using Wasserstein Generative Adversarial Networks Gradient Penalty (WGAN-GP).
  • To enable opportunistic maintenance through component clustering and optimize decisions using a cost-aware grid search.

Main Methods:

  • Component-specific RUL prediction using Long Short-Term Memory (LSTM) models.
Keywords:
ClusteringData scarcityGAN-based data augmentationMaintenance optimizationMaintenance schedulingPredictive maintenance (PdM)WGAN-GP

Related Experiment Videos

  • Data augmentation for failure data sparsity with WGAN-GP.
  • Component clustering using Density-Based Clustering Space (DBSCAN) for opportunistic maintenance.
  • Cost-aware grid search optimization based on RUL distributions and risk proxy.

    • Main Results:

    • The proposed approach significantly reduces corrective failures and normalized maintenance costs.
    • Empirical results show continuous cost reduction compared to non-clustering methods.
    • Sensitivity analysis confirms consistent optimal maintenance levels across various cost assumptions, highlighting economic resilience.

    Conclusions:

    • The study presents a robust and scalable PdM framework combining data augmentation, clustering, and risk-aware optimization.
    • This approach advances PdM towards more practical and cost-effective decision-making in industrial settings.
    • The integrated method enhances maintenance strategies by considering RUL, component similarity, and economic factors.