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Remaining service life prediction based on gray model and empirical Bayesian with applications to compressors and pumps
Quality and Reliability Engineering International ( IF 2.3 ) Pub Date : 2020-09-09 , DOI: 10.1002/qre.2756
Xiaochuan Li 1 , David Mba 1 , Edmund Okoroigwe 2 , Tianran Lin 3
Affiliation  

In this study, a three‐step remaining service life (RSL) prediction method, which involves feature extraction, feature selection, and fusion and prognostics, is proposed for large‐scale rotating machinery in the presence of scarce failure data. In the feature extraction step, eight time‐domain degradation features are extracted from the faulty variables. A fitness function as a weighted linear combination of the monotonicity, robustness, correlation, and trendability metrics is defined and used to evaluate the suitability of the features for RSL prediction. The selected features are merged using a canonical variate residuals‐based method. In the prognostic step, gray model is used in combination with empirical Bayesian algorithm for RSL prediction in the presence of scarce failure data. The proposed approach is validated on failure data collected from an operational industrial centrifugal pump and a compressor.

中文翻译:

基于灰色模型和经验贝叶斯的剩余使用寿命预测及其在压缩机和泵中的应用

在这项研究中,针对缺少故障数据的大型旋转机械,提出了一种三步剩余使用寿命(RSL)预测方法,其中涉及特征提取,特征选择以及融合和预测。在特征提取步骤中,从故障变量中提取了八个时域降级特征。定义适合度函数作为单调性,鲁棒性,相关性和趋势性度量的加权线性组合,并将其用于评估特征对RSL预测的适用性。所选要素使用基于规范变异残差的方法合并。在预后步骤中,在缺乏故障数据的情况下,将灰色模型与经验贝叶斯算法结合用于RSL预测。
更新日期:2020-09-09
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