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Reliability analysis of freight elevator systems based on FFMEC, FTA and BN.

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Summary

This study enhances large-tonnage freight elevator (LTFE) reliability in textile industries using a combined FFMECA, FTA, and BN approach. The method successfully identified critical failure modes, leading to a system reliability exceeding 94%.

Keywords:
Bayesian networksFFMECAFTALTFERisk assessment and prevention

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Area of Science:

  • Engineering
  • Industrial Systems
  • Risk Management

Background:

  • Reliability design is crucial for complex systems like large-tonnage freight elevators (LTFE).
  • The textile dyeing and printing industry requires high operational reliability for LTFE.
  • Existing reliability analysis methods may not fully capture the complexities of LTFE failure modes.

Purpose of the Study:

  • To propose a comprehensive reliability analysis method for LTFE in the textile industry.
  • To identify and prioritize high-risk failure modes in LTFE.
  • To improve the overall system reliability of LTFE.

Main Methods:

  • Fuzzy Failure Mode, Effect and Criticality Analysis (FFMECA) using Fuzzy Analytic Hierarchy Process (FAHP) and entropy-weight method.
  • Fault Tree Analysis (FTA) to model failure causes.
  • Bayesian Networks (BN) for inferential analysis and mapping from FTA.
  • Calculation of Fuzzy Risk Priority Number (FRPN) to rank failure modes.

Main Results:

  • Six high-risk failure modes were identified, including gate structure faults, tractor failure, and electromagnetic braking faults.
  • The integrated FFMECA, FTA, and BN approach effectively identified critical failure points.
  • Proposed improvement measures were simulated and validated.

Conclusions:

  • The developed comprehensive analysis method significantly improves LTFE reliability.
  • The system reliability achieved over 94%, meeting design requirements.
  • This approach provides a robust framework for enhancing the safety and efficiency of complex industrial systems.