Session: 02-12-01 Reliability Based Maintenance 1

Paper Number: 124269

124269 - Digital Twin for Floating Offshore Wind Foundations Operation and Maintenance Management 

In the offshore business, environmental conditions typically have a significant effect on the operation performance and maintenance, and the total costs. E.g. in offshore wind power, depending naturally on the unit location, operation and maintenance (O&M) costs can sum up to almost 30% of the total cost of energy. Thus, monitoring of the affecting environmental and operation conditions are critical from reliability point of view, and the basis for cost-effective maintenance management and decision-making. Early identification of the most loaded parts and impending faults leaves time for inspection and maintenance planning and execution within suitable conditions that are fulfilling the criteria e.g. for human and robot based operations. In this study, predictive maintenance tools were developed in the context of a floating structure with features representative of the maintenance needs in floating offshore wind foundations. The structure – a cylindrical buoy that was moored to the harbour in Viana do Castelo in Portugal - was measured dynamically and statistically over the local weather conditions. Based on the excitation data (waves) and response data (acceleration, inclination and pitch/roll/heave), and main structural dimensions, a virtual model (Ansys AQWA) of the testbed was built. The built virtual model was utilized as digital twin for conducting in wider environmental perspective frequency and time domain simulations to evaluate hydrodynamic behavior, motions, and accelerations, including hydrodynamic and mooring line loads. The main analysed environmental conditions represent conditions at the Atlantic Ocean on the coast of Portugal. In this study, the virtual model simulated data were used for weather window and mooring line maintenance need assessments. The weather window is a time during which the environmental conditions allow execution of a specific marine operation. Based on these assessments, it is shown how data and tools can be used for determining suitable window for human and robotic-based inspections utilizing simulated motions of a structural point selected for maintenance. In marine environment, one major factor restricting O&M activities is exposure to dynamic motion. For the assessment of working environment, a simplified, limiting motion criteria was utilized for heavy manual work. The exposure indicator is based on short-length moving root-mean-square values of both vibration acceleration and roll/pitch. For these indicators, data profiling was applied. One convenient method for operability analysis is time-at-level (TAL). It counts the time spent in different operating regions valid for both operational state and history analysis. In the long term, the analysis can be used for statistical analysis and comparison with other units. In the short term, it can be used to evaluate, e.g. based on weather forecasts, the probability for maintenance weather window and the suitability for maintenance deployment defined by operation criteria and expected exposure time. Ahead, the simulated data was used for mooring line loading and remaining useful life (RUL) analysis, for providing deterministic decision-making support for deployment of robots for justified inspections, and for structural health monitoring (SHM). The support for inspection deployment was demonstrated by accumulating the load cycles within defined loading ranges, which were then further transformed by summing up individual damaging loading shares to computational time until the next inspection/maintenance. The effect of analysis and loading accumulation was shown on mooring line cables in different marine sea state conditions. The adaptation of the demonstrated methods could help the operator to take actions to execute inspections, and to transform the maintenance strategy from reactive maintenance to proactive, condition-based maintenance by monitoring and predicting actual condition of the critical parts and to prevent unnecessary maintenance, and thus to reduce the maintenance costs. The demonstrated methods can be also used for flagging e.g. sea state conditions leading to premature failures and reduced lifetime.

Presenting Author: Jari Halme VTT Technical Research Centre of Finland Ltd

Presenting Author Biography: Mr. Jari Halme (male) is Senior Scientist in Operation and Maintenance team at VTT. He received Master’s Degree (Tech.) in 1995. Since 1995 he has been employed by VTT having various project responsibilities related to maintenance, condition monitoring and diagnosis of rotating machinery. Currently he is responsible for development of substance excellence in the VTT Predictive maintenance focus area, and to support the decision making for industrial circular economy. He has been project manager of several national and international research projects, and he has obtained 2011 a IPMA Level-C certification for the professional management of projects. He has been a member of diagnostics committee in the Finnish Maintenance Society. He is co-author of several books and author or co-author of more than 50 research papers in the field of condition monitoring, diagnosis and prognosis, and e-maintenance.

Authors:

Jari Halme VTT Technical Research Centre of Finland Ltd
Ieza Souza Ramos VTT Technical Research Centre of Finland Ltd
Eeva Mikkola VTT Technical Research Centre of Finland Ltd