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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Distribution Reliability and Automation

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Related Experiment Videos

Temporally rigorous and traceable predictive maintenance via joint labeler-model optimization.

Maytha Al-Ali1, Ahmad Alharbi2

  • 1College of Business, Zayed University, Dubai, 19282, UAE.

Scientific Reports
|May 18, 2026
PubMed
Summary

This study introduces a unified framework for predictive maintenance (PdM) that optimizes labeling and model parameters, ensuring temporal accuracy for intelligent asset management in Industry 4.0. It achieves state-of-the-art results, enhancing reliability and traceability.

Keywords:
Failure predictionIndustry 4.0Machine learning (ML)Maintenance decision supportPredictive maintenance (PdM)

Related Experiment Videos

Area of Science:

  • Industrial Engineering
  • Machine Learning
  • Data Science

Background:

  • Predictive maintenance (PdM) is crucial for Industry 4.0 asset management but faces challenges in operationalization due to methodological fragmentation.
  • Existing PdM frameworks often compromise temporal realism and class granularity for speed, and decouple labeling from hyperparameter optimization.
  • Reproducibility and deployment traceability, especially for rare failures, are often insufficient in current PdM solutions.

Purpose of the Study:

  • To propose a unified, end-to-end, and fully traceable PdM framework.
  • To jointly optimize labeling strategy and model hyperparameters while enforcing strict temporal fidelity.
  • To address limitations of existing frameworks, particularly in rare-failure scenarios and operational deployment.

Main Methods:

  • A novel pipeline co-optimizes the failure lookahead window (τ) and LightGBM hyperparameters using Bayesian optimization with Optuna.
  • Rigorous temporal leakage prevention is achieved through forward-chaining cross-validation and disjoint temporal holdout evaluation.
  • The framework ensures reproducibility by exporting versioned artifacts like models, preprocessors, and configurations.

Main Results:

  • Achieved state-of-the-art performance on the Fidan dataset with a macro-F1 score of 0.9875 and balanced accuracy of 0.9915.
  • Demonstrated superior PR-AUC compared to prior benchmarks, validating the effectiveness of the unified approach.
  • Computational analysis and ablation studies confirmed the framework's scalability and the significance of its design choices.

Conclusions:

  • The proposed unified framework significantly enhances predictive maintenance capabilities by integrating labeling and model optimization with temporal fidelity.
  • The framework provides a traceable and reproducible solution, facilitating enterprise integration and supporting intelligent asset management.
  • Future work includes site-specific validation for operational impact quantification, such as reduced downtime and optimized dispatching.