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With the development of offshore wind power, the reliability analysis of offshore wind turbines is increasingly significant due to the system complexity and negative impacts in harsh operating conditions. In this study, the Fault Tree Analysis method is adopted for both qualitative and quantitative evaluation of semi-submersible floating offshore wind turbine failure characteristics. The floating offshore wind turbine is divided into several assemblies, including support structures, pitch and hydraulic system, gearbox, generator and the other systems. Failure rates of relevant offshore structures are collected from previous studies, reports and reliability databases. On this basis, the quantitative assessment of Minimum Cut Sets and Importance Measures are achieved. The calculated results are generally in conformity with statistical data, indicating that most of the failures are caused by several basic factors. Marine conditions, especially the salt-spray and high wind speed, show the most significant impact on floating offshore wind turbine performance.

A well-established condition-based maintenance (CBM) method based on condition monitoring information can be used to reduce maintenance costs by decimating unnecessary maintenance actions, reducing system downtime, and minimizing unexpected failures. In this paper, we propose an opportunistic CBM optimization approach for offshore wind turbines (OWTs) in which economic dependence exists among the components that are subjected to condition monitoring. An artificial neural network is used to predict life percentage by leveraging the condition monitoring information. A conditional failure probability value that is derived from the predicted failure-time distribution of the component was adopted to represent the deterioration of OWTs. Our maintenance method can be defined by a threshold with two-level failure probability. We propose a simulation method that can be used to calculate the optimal threshold values to minimize the long-term maintenance cost. Failure information and maintenance cost of OWTs are collected from existing articles to illustrate the proposed approach. Results show that the opportunistic CBM strategy can be effective and is established in the wind power industry. Moreover, the expense comparison between onshore and offshore WTs demonstrates the importance of this method.

Dropped objects is one of the most hazardous accidents of floating production storage offloading (FPSO) due to the potentially severe consequences, therefore, the quantitative calculation of dropped objects impact risk, which is featured with uncertainties, is essential. Monte Carlo Simulation (MCS) is a method for this intention with flexibility and efficiency. However, wide calculating resources are needed for MCS-based accident analysis, especially when multiple random variables are concerned. Moreover, dropped objects collision damage models are perplexed and usually pronounced by the Finite Element Method (FEM), which is of a computational complexity. To address this issue, this paper considered a combined methodology: the Artificial Neural Network (ANN) adjusted by genetic algorithm (GA) is united with MCS to analyze dropped objects collision failure probability quantitatively. A time-dependent progressive simulation is employed to forecast the structural response caused by dropped objects collision. Then the ANN-GA is trained based upon the collision data and used as an alternative for the computational FEM performs with the MCS methodology. The risk level for the dropped objects collision of FPSO is assessed by comparing the proposed method and the DNV rules, and a qualitative uncertainty analysis is also conducted in this quantitative failure analysis.

Risk assessment of Floating Offshore Wind Turbines (FOWTs) relies on failure statistics, which are usually collected from various source. In order to quantify the information uncertainties, this paper introduces the Fuzzy Fault Tree Analysis (FFTA) to assess the risk of semi-submersible FOWTs. A Fault Tree (FT) model is adopted to analyze the risk evolution mechanism of the FOWT and the fuzzy set theory is employed to process the failure statistics. As the results of quantitative analysis, the failure intervals of the FOWT and critical assemblies are calculated. The risk measure named Fuzzy Critical Importance (FCI) is utilized to compute the sequence of the basic fault events and identify critical components. A comparison analysis is performed and the results indicate that the present FFTA method is more in line with practical and has the merit in error reduction of failure rate figured out.

This paper presents a Generalized Stochastic Petri Net (GSPN) approach to assess risk of FPSO topsides, considering the weather window and imperfect maintenance effect. The FPSO topside is considered as a multi-component system consisting of several assemblies. A GSPN model is developed according to system identification and the correlation between different components. The reliability, availability, and maintenance information of the system are derived using the model, based on failures collected from existing literature and database. An operation and maintenance cost model is established, and an optimized maintenance strategy is finally determined based on the GSPN outcomes. The results indicate that the proposed approach is an effective tool to evaluate the system risk and save the maintenance expenditure of FPSO topsides.

Floating offshore wind turbine (FOWT) is complexly structured by interdependent subsystems and experiences negative impacts in harsh operating conditions. During the risk and reliability analysis, two issues have to be addressed: system failure mode complexity and mutual correlation. We conducted risk assessment through a modified Failure Modes and Effects Analysis (FMEA) method, named correlation-FMEA,to study the connection between failure modes and its effect on the failure probability of the entire system. A series of failure modes with high priority were determined by conventional FMEA, and the corresponding connections were analyzed to obtain the correlation coefficients using the reliability index vector method. The data used in our research comes from field operation in China. Probability Network Evaluation Technique (PNET) was used to get the weakest failure modes set of the system based on those coefficients. With the results, suggestions for floating wind turbine design were provided regarding aspects of safety and reliability.

Operation and maintenance (O&M) costs account for a large proportion of the total costs for offshore wind energy. Performing a reasonable maintenance strategy is an effective approach to reduce O&M costs and gain more profits. In this paper, an opportunistic maintenance strategy for offshore wind turbine systems considering maintenance intervals of each subsystem is proposed to minimize the total maintenance cost. First, a Non-homogeneous Continuous-Time Markov Process based state transition model is established to study degradation process of subsystems. The influence of maintenance time schedule on the maintenance cost is studied to obtain the optimal maintenance intervals of each subsystems. Then, an opportunistic maintenance model considering economic dependencies between multiple subsystems is proposed to optimize the maintenance strategy by combining maintenance activities of individual subsystems to a grouping maintenance activity. A numerical example is used to indicate the significant effectiveness of the maintenance model. The result shows that the total maintenance cost of an offshore wind turbine system will be reduced by adopting the opportunistic maintenance strategy when compared with conventional preventive maintenance strategy.