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July 26, 2026Journal of Quality in Maintenance Engineering

A hybrid framework for data-driven predictive maintenance: probabilistic RUL estimation and early failure signaling via control charts

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Authors

ARAhmad RazaviMRMohammad Reza RasouliMPMir Saman Pishvaee

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Overview

Randomized trial develops and validates predictive maintenance framework to enhance equipment efficiency, suggesting improved maintenance strategies.

Key Points

  • This study aims to create a predictive maintenance framework that accurately estimates the remaining useful life of equipment and provides timely maintenance signals.
  • Developed a data-driven predictive maintenance framework combining statistical modeling and machine learning techniques.
  • Utilized Weibull distribution for RUL modeling and integrated random forest and EWMA control charts for predicting failures.
  • Validated methodology using a synthetic dataset simulating real-world conditions.
  • Framework achieved high accuracy in estimating remaining useful life of equipment, with timely maintenance alerts.
  • Predictions of failure probabilities were generated well in advance, allowing effective maintenance planning.
  • Integration of Weibull distribution and Random Forest classifiers improved prediction reliability and monitoring efficiency.

Cite This Study

Razavi et al. (2026) studied this question.

synapsesocial.com/papers/6a65a84ed3aea3239cd78cbbhttps://doi.org/10.1108/jqme-01-2025-0003
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Also Consider

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  1. 1A two-stage framework for cost-sensitive predictive maintenance using deep learning, GANs, and risk-aware clustering2026
  2. 2A degradation index model for maintenance prediction run-to-failure in production systems2026
  3. 3A degradation modelling framework integrating prediction uncertainty and imperfect maintenance for maintenance strategy optimisation2026
  4. 4ADVANCED PREDICTIVE MAINTENANCE FRAMEWORK FOR INDUSTRIAL EQUIPMENT BASED ON HYBRID MACHINE LEARNING AND EDGE COMPUTING ARCHITECTURES2026
  5. 5Remaining Useful Life ( <scp>RUL</scp> ) Prediction Methods for Machine Health Estimation and Fault Diagnosis: A Comprehensive Review of Latest Techniques and Future Prospects2026 · 1 citations