A data-driven method for estimating the remaining useful life of a Composite Drill Pipe

被引：0

作者：

Lahmadi, Ahmed ^{[1
]}

Terrissa, Labib ^{[2
]}

Zerhouni, Noureddine ^{[3
]}

机构：

[1] LINATI Lab, Ouargla, Algeria

[2] LINFI Lab, Biskra, Algeria

[3] Femto ST, Besancon, France

来源：

2018 INTERNATIONAL CONFERENCE ON ADVANCED SYSTEMS AND ELECTRICAL TECHNOLOGIES (IC_ASET) | 2017年

关键词：

Data-driven approach; composites; drill pipe; neural networks;

D O I：

暂无

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：

Composite drill pipe has known a big interest in the shortradius drilling industry due to its lightweight, flexibility and the performance properties of steel pipe Despite its benefits, composites suffer from fatigue when subjected to loads, which leads to failure. To prevent this the use of a predictive maintenance monitoring the state and predicting its remaining useful life is needed. In this work, we proposed a predictive maintenance method for estimating the remaining useful life of composite drill pipe subjected to cyclic loads. A tension-tension fatigue experiment in a cross-ply Carbon fiber reinforced polymer (CFRP) laminate is used for case study

引用

页码：192 / 195

页数：4

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