Online System Prognostics with Ensemble Models and Evolving Clustering

被引：1

作者：

Tseng, Fling ^{[1
]}

Filev, Dimitar ^{[1
]}

Yildirim, Murat ^{[2
]}

Chinnam, Ratna Babu ^{[2
]}

机构：

[1] Ford Motor Co, Modern Control Methods & Computat Intelligence, Res & Adv Engn, 2101 Village Rd, Dearborn, MI 48121 USA

[2] Wayne State Univ, Dept Ind & Syst Engn, 4815 Fourth St, Detroit, MI 48202 USA

来源：

MACHINES | 2023年 / 11卷 / 01期

关键词：

system prognostics; evolving clustering; survivability estimation; remaining useful life estimation; WEIBULL DISTRIBUTION; TIME-SERIES; FUZZY; IDENTIFICATION; MACHINERY; ALGORITHM; DIAGNOSIS;

D O I：

10.3390/machines11010040

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

An online evolving clustering (OEC) method equivalent to ensemble modeling is proposed to tackle prognostics problems of learning and the prediction of remaining useful life (RUL). During the learning phase, OEC extracts predominant operating modes as multiple evolving clusters (EC). Each EC is associated with its own Weibull distribution-inspired degradation (survivability) model that will receive incremental online modifications as degradation signals become available. Example case studies from machining (drilling) and automotive brake-pad wear prognostics are used to validate the effectiveness of the proposed method.

引用

页数：34

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