Remaining useful life estimation of HMPE rope during CBOS testing through machine learning

OCEAN ENGINEERING

卷:238

Article

Falconer, Shaun¹  Nordgard-Hansen, Ellen²  Grasmo, Geir¹ 

[1] Univ Agder, Dept Engn Sci, N-4876 Grimstad, Norway

[2] NORCE Norwegian Res Ctr AS, N-4876 Grimstad, Norway

关键词: Fibre rope;    Offshore lifting;    Remaining useful life;    Machine learning;   

DOI  :  10.1016/j.oceaneng.2021.109617

来源: Elsevier

【 摘 要 】

Fibre rope used in cranes for offshore deployment and recovery has significant potential to perform lifts with smaller cranes and vessels to reach depths limited by weight of steel wire rope. Current condition monitoring methods based on manual inspection and time-based and reactive maintenance have significant potential for improvement coupled with more accurate remaining useful life (RUL) prediction. Machine learning has found use as a condition monitoring approach, coupled with vast improvements in data acquisition methods. This paper details data-driven RUL prediction methods based on machine learning algorithms applied on cyclic-bend-over-sheave (CBOS) tests performed on two fibre rope types until failure. Data extracted through computer vision and thermal monitoring is used to predict RUL through neural networks, support vector machines and random forest. Random forest and neural networks methods are shown to be particularly adept at predicting RUL compared to support vector machines . Additionally, improved RUL predictions can be achieved by combining data from distinct rope types subject to different test conditions.

【 授权许可】

Free