Online Performance Assessment Method for a Model-Based Prognostic Approach

被引：55

作者：

Hu, Yang ^{[1
]}

Baraldi, Piero ^{[1
]}

Di Maio, Francesco ^{[1
]}

Zio, Enrico ^{[1
,2
]}

机构：

[1] Politecn Milan, Dept Energy, I-20156 Milan, Italy

[2] Ecole Cent Paris & Supelec, European Fdn New Energy Elect France, Chair Syst Sci & Energet Challenge, Paris, France

来源：

IEEE TRANSACTIONS ON RELIABILITY | 2016年 / 65卷 / 02期

关键词：

Kernel smoothing; model-based prognostics; online prognostics performance assessment; particle filter; turbine blade creeping; FILTER-BASED PROGNOSTICS; DEMPSTER-SHAFER THEORY; REMAINING USEFUL LIFE; DATA-DRIVEN; DEGRADATION; PREDICTION; DAMAGE; STATE; MAINTENANCE; ALGORITHMS;

D O I：

10.1109/TR.2015.2500681

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we propose a method for online assessing the performance of a prognostic approach in situations of very poor knowledge on the degradation process. In particular, we deal with cases in which the entire degradation process, from the beginning of the operation until failure, has never been observed and, thus, the traditional offline performance metrics cannot be applied. The proposed method is applied on a prognostic approach based on a particle filter and optimized tuning kernel smoothing (PF-OTKS). Case studies regarding the degradation of turbine blade and aluminum electrolytic capacitor are considered.

引用

页码：718 / 735

页数：18

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