A Study on Prognostics Approach for Estimating the RUL of Mooring Line

Citations Over Time

Abstract

The failure of a mechanical system could lead to enormous social and economic damage. To address this problem, companies have carried out various maintenance policies such as corrective maintenance, preventive maintenance, risk-based maintenance, and so on. Recently, thanks to the emerging information and communication technologies such as internet-of-things, wireless tele-communication technologies, and so on, the condition-based maintenance (or predictive maintenance, prognostics and health management) policy has been highlighted. The condition-based maintenance could take the suitable maintenance action before a failure occurs based on the condition of the target system. In general, the condition-based maintenance system has the capability to monitor the state of the mechanical system in real time, and detect the system abnormality at an early stage, and predict the future occurrence of failure in advance. To this end, it is critical to develop the suitable monitoring, diagnostics, and prognostics methods for the target system. In this study, we will propose an approach to predict RUL (Remaining Useful Lifetime) of offshore plant equipment (mooring line) based on gathered sensor data. To predict the RUL, several time-series forecasting methods have been applied and evaluated. In conclusion, we have summarized our study and discussed the limitations of the proposed approach.

Related Papers

• → An Approach for Condition-Based Maintenance Optimization Applied to Wind Turbine Blades(2010)229 cited
• → Condition based maintenance in the context of opportunistic maintenance(2012)120 cited
• → Condition assessment of 500/150 kV power transformer based on condition based maintenance(2011)15 cited
• → Optimal maintenance of substation equipment by considering maintenance cost and reliability(2014)7 cited
• Condition-based maintenance for effective and efficient rolling stock capacity assurance : A study on heavy haul transport in Sweden(2014)