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首页> 外文期刊>Reliability Engineering & System Safety >Industrial equipment reliability estimation: A Bayesian Weibull regression model with covariate selection
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Industrial equipment reliability estimation: A Bayesian Weibull regression model with covariate selection

机译：工业设备可靠性估算：具有协变量选择的贝叶斯威布尔回归模型

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摘要

A three-state continuous-time semi-Markov process is used to model the degradation of an industrial equipment. The transition times are assumed Weibull-distributed and influenced by a set of covariates. A Weibull Regression Model is developed within the Bayesian probability framework, to account for the influence of these covariates and estimate the model parameters with the related uncertainty, on the basis of few data and expert judgment. The number of covariates is reduced by a two-step selection procedure derived from the condition monitoring engineering practice. The developed model enables estimating reliability and time-dependent state probabilities for a component degrading in given operational and ambient conditions, represented by a vector of covariates. The model is illustrated by way of a real case study concerning the degradation process affecting diaphragm valves used in the biopharmaceutical industry.
机译：三态连续时间半马尔可夫工艺用于模拟工业设备的劣化。过渡时间被假定威布尔分布并受到一组协变量的影响。在贝叶斯概率框架内开发了威布尔回归模型，以考虑这些协变量的影响，并在几个数据和专家判断的基础上估计与相关的不确定性的模型参数。通过从条件监测工程实践中得出的两步选择过程减少了协变量的数量。开发的模型使得能够在给定的操作和环境条件下估计可靠性和时间相关的状态概率，由协变量的矢量表示。该模型通过关于影响生物制药工业中使用的隔膜阀的降解过程的实际研究来说明。

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