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Fatigue01:21

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Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...
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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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Remaining Useful Life Prediction Method for Stochastic Degrading Devices Considering Predictive Maintenance.

Qing Dong1, Hong Pei1, Changhua Hu1

  • 1The Department of Automation, Rocket Force University of Engineering, Xi'an 710025, China.

Sensors (Basel, Switzerland)
|February 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive remaining useful life (RUL) prediction method for repairable devices, accounting for maintenance impacts. The approach enhances RUL prediction accuracy and adaptability for future degradation processes.

Keywords:
adaptive Wienerimperfect maintenancepredictive maintenanceremaining useful liferepairable degrading devicessensor-based monitoring

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Area of Science:

  • Engineering
  • Reliability Engineering
  • Data Science

Background:

  • Predictive maintenance is crucial for device longevity but often overlooks maintenance's effect on sensor data and RUL for repairable systems.
  • Existing RUL prediction methods may lack accuracy due to unaddressed impacts of preventive maintenance on degradation.
  • Repairable degrading devices present unique challenges in RUL estimation due to maintenance interventions.

Purpose of the Study:

  • To develop an adaptive remaining useful life (RUL) prediction method for repairable degrading devices.
  • To improve the accuracy and adaptability of RUL predictions by considering the impact of imperfect maintenance.
  • To provide a robust framework for health management of degrading systems.

Main Methods:

  • Established a phased degradation model using an adaptive Wiener process to incorporate imperfect maintenance effects.
  • Derived the probability distribution of RUL based on the first hitting time (FHT) concept, integrating maintenance impacts on degradation.
  • Developed a model parameter identification and updating method using maximum likelihood estimation and Bayesian inference, accounting for device variations.

Main Results:

  • The proposed adaptive RUL prediction method effectively accounts for the influence of maintenance on degradation.
  • The model demonstrates improved accuracy and adaptability in predicting RUL for repairable devices.
  • Validation through numerical simulations and real-world gyroscope degradation data confirms the method's effectiveness.

Conclusions:

  • The adaptive RUL prediction method offers a significant advancement in managing repairable degrading devices.
  • Accounting for maintenance impacts is essential for accurate RUL prediction in such systems.
  • The developed approach enhances device lifespan management and operational reliability.