Bayesian Reliability Assessment of Permanent Magnet Brake Under Small Sample Size

Permanent magnet brakes (PMBs) have diverse applications in automotive, robotics, medical devices, and aerospace industries. However, evaluating the reliability of PMBs is challenging due to their complex construction and limited data availability. This article proposes a bivariate Wiener model to c...

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Bibliographic Details
Published in:IEEE transactions on reliability Vol. 74; no. 1; pp. 2107 - 2117
Main Authors: Xu, Ancha, Wang, Binbing, Zhu, Di, Pang, Jihong, Lian, Xinze
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Bayes methods
Bayesian analysis
Bivariate analysis
Bivariate Wiener process
Brakes
Degradation
Mean time to failure
Monte Carlo
Permanent magnets
reference prior
rejection sampling
Reliability
Reliability analysis
Robotics
Simulation
Stress
Thermal stability
Torque
ISSN:0018-9529, 1558-1721
Online Access:Get full text
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Summary:Permanent magnet brakes (PMBs) have diverse applications in automotive, robotics, medical devices, and aerospace industries. However, evaluating the reliability of PMBs is challenging due to their complex construction and limited data availability. This article proposes a bivariate Wiener model to capture the degradation patterns of two key performance characteristics of PMBs, and introduces an objective Bayesian method to analyze degradation data with small sample sizes. The derivation of Jeffery prior and reference priors for different ordering groups, along with the verification of posterior propriety, are presented. A rejection sampling embedded Monte Carlo algorithm is employed to obtain Bayesian estimates of model parameters, reliability, and mean-time-to-failure of PMBs. Simulation study results demonstrate the superiority of the objective Bayesian method over the maximum likelihood approach, particularly when dealing with small sample sizes. Finally, the proposed method is applied to assess the reliability of PMBs.
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ISSN:0018-9529
1558-1721
DOI:10.1109/TR.2024.3381072