Predictive information and maintenance optimization based on decision theory: a case study considering a welded joint in an offshore wind turbine support structure

2021 ◽  
pp. 147592172098183
Author(s):  
Muhammad Farhan ◽  
Ronald Schneider ◽  
Sebastian Thöns
Keyword(s):  
Service Life ◽  
Wind Turbine ◽  
Value Of Information ◽  
Welded Joint ◽  
Offshore Wind ◽  
Inspection Time ◽  
Offshore Wind Turbine ◽  
Maintenance Optimization ◽  
Predictive Information ◽  
Repair Method

Predictive information and maintenance optimization for deteriorating structures is concerned with scheduling (a) the collection of information by inspection and monitoring and (b) maintenance actions such as repair, replacement, and retrofitting based on updated predictions of the future condition of the structural system. In this article, we consider the problem of jointly identifying—at the beginning of the service life—the optimal inspection time and repair strategy for a generic welded joint in a generic offshore wind turbine structure subject to fatigue. The optimization is performed based on different types of decision analyses including value of information analyses to quantify the expected service life cost encompassing inspection, repair, and fatigue damage for all relevant combinations of inspection time, repair method, and repair time. Based on the analysis of the expected service life cost, the optimal inspection time, repair method, and repair time are identified. Possible repair methods for a welded joint in an offshore environment include welding and grinding, for which detailed models are formulated and utilized to update the joint’s fatigue performance. The decision analyses reveal that an inspection should be scheduled approximately at mid-service life of the welded joint. A repair should be performed in the same year after an indication and measurement of a fatigue crack given an optimal inspection scheduling. This article concludes with a discussion on the results obtained from the decision and value of information analyses.

Reliability of Offshore Wind Turbine Support Structures Subjected to Extreme Wave-Induced Loads and Defects

2016 ◽  
Author(s):  
Baran Yeter ◽  
Yordan Garbatov ◽  
C. Guedes Soares
Keyword(s):  
Service Life ◽  
Wind Turbine ◽  
Crack Growth ◽  
Low Cycle Fatigue ◽  
Limit State ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Cycle Fatigue ◽  
Support Structures ◽  
Support Structure

The probability of existence of defects, fatigue damage and crack growth in the offshore wind turbine support structures subjected to extreme waves and wind-induced loads is very high and may occur at a faster rate in a low cycle fatigue regime and crack growth, leading to a dramatic reduction in the service life of structures. It is therefore vital to assess the safety and reliability of offshore wind turbine support structures at sea. The aim of the present study is to carry out a low cycle fatigue and crack growth reliability analysis of an offshore wind turbine support structure during the service life. The analysis includes different loading scenarios and accounts for the uncertainties related to the structural geometrical characteristics, the size of the manufacturing and during the service life defects, crack growth, material properties, and model assumed in the numerical analyses. The probability of failure is defined as a serial system of two probabilistic events described by two limit state functions. The first one is related to a crack initiation based on the local strain approach and the second one on the crack growth applying the fracture mechanic approach. The first and second order reliability methods are used to estimate the reliability index and the effect of low cycle fatigue and crack growth on the reliability estimate of the offshore wind turbine support structure. The sensitivity analysis is performed in order to determine the degree of the significance of the random variables and several conclusions are derived.

scholarly journals Updating Failure Probability of a Welded Joint in Offshore Wind Turbine Substructures

2016 ◽  
Author(s):  
Quang A. Mai ◽  
John D. Sørensen ◽  
Philippe Rigo
Keyword(s):  
Wind Turbine ◽  
Welded Joint ◽  
Limit State ◽  
Offshore Wind ◽  
Design Stage ◽  
Maintenance Cost ◽  
Offshore Wind Turbine ◽  
Material Strength ◽  
State Function ◽  
Inspection Planning

The operation and maintenance cost of offshore wind turbine substructures contributes significantly in the cost of a kWh. That cost may be lowered by application of reliability- and risk-based maintenance strategies and reliability updating based on inspections performed during the design lifetime. Updating the reliability of a welded joint can theoretically be done using Bayesian updating. However, for tubular joints in offshore wind turbine substructures when considering a two dimensional crack growth and a failure criterion combining brittle fracture and material strength, the updating is quite complex due to the wind turbine loading obtained during operation. This paper solves that updating problem by using the Failure Assessment Diagram as a limit state function. It is discussed how application of the updating procedure can be used for inspection planning for offshore wind turbine substructures, and thus also for reducing the required safety factors at the design stage.

Experimental Study for SPAR Type Floating Offshore Wind Turbine With Blade-Pitch Control

2013 ◽  
Author(s):  
Toshiki Chujo ◽  
Yoshimasa Minami ◽  
Tadashi Nimura ◽  
Shigesuke Ishida
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Offshore Wind ◽  
Scale Model ◽  
Offshore Wind Turbine ◽  
Experimental Proof ◽  
Pitch Control ◽  
Model Experiments ◽  
North Eastern ◽  
North Eastern Part

The experimental proof of the floating wind turbine has been started off Goto Islands in Japan. Furthermore, the project of floating wind farm is afoot off Fukushima Prof. in north eastern part of Japan. It is essential for realization of the floating wind farm to comprehend its safety, electric generating property and motion in waves and wind. The scale model experiments are effective to catch the characteristic of floating wind turbines. Authors have mainly carried out scale model experiments with wind turbine models on SPAR buoy type floaters. The wind turbine models have blade-pitch control mechanism and authors focused attention on the effect of blade-pitch control on both the motion of floater and fluctuation of rotor speed. In this paper, the results of scale model experiments are discussed from the aspect of motion of floater and the effect of blade-pitch control.

scholarly journals Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3333
Author(s):  
Maria del Cisne Feijóo ◽  
Yovana Zambrano ◽  
Yolanda Vidal ◽  
Christian Tutivén
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Environmental Conditions ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Small Subset ◽  
Scaled Model ◽  
Vibration Data ◽  
Large Water

Structural health monitoring for offshore wind turbine foundations is paramount to the further development of offshore fixed wind farms. At present time there are a limited number of foundation designs, the jacket type being the preferred one in large water depths. In this work, a jacket-type foundation damage diagnosis strategy is stated. Normally, most or all the available data are of regular operation, thus methods that focus on the data leading to failures end up using only a small subset of the available data. Furthermore, when there is no historical precedent of a type of fault, those methods cannot be used. In addition, offshore wind turbines work under a wide variety of environmental conditions and regions of operation involving unknown input excitation given by the wind and waves. Taking into account the aforementioned difficulties, the stated strategy in this work is based on an autoencoder neural network model and its contribution is two-fold: (i) the proposed strategy is based only on healthy data, and (ii) it works under different operating and environmental conditions based only on the output vibration data gathered by accelerometer sensors. The proposed strategy has been tested through experimental laboratory tests on a scaled model.

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