Sebastian Thöns

Integrated monitoring of offshore wind turbines - requirements, concepts and experiences

Wind turbines on offshore sites (OWECs) are subjected to combined loads from wind and waves. These dynamic loads, with a frequency content within the range of the natural frequencies of the structures, cause fatigue-effective stresses in the substructures of wind turbines. Therefore, the examination of natural frequencies is an important part within the design process of wind turbines. The quality of the numerical models for such calculations is of great importance, since the certification guidelines permit only small uncertainties in modal analysis results. The accuracy of the parameters of the numerical model can only be achieved through a comparison of simulation results with corresponding test results. Therefore, it is necessary to measure the dynamic behaviour of all components of the wind turbines simultaneously. This is true not only for the design verification, but also for monitoring the OWECs in operation. The potential of integrated systems for monitoring-based maintenance optimisation should thus be used.

Quantification of the value of structural health monitoring information for fatigue deteriorating structural systems

This paper addresses the quantification of the value of structural health monitoring (SHM) before its implementation for structural systems on the basis of its Value of Information (VoI). The value of SHM is calculated utilizing the Bayesian pre-posterior decision analysis modelling the structural life cycle performance, the integrity management and the structural risks. The relevance and precision of SHM information for the reduction of the structural system risks and the expected cost of the structural integrity management throughout the life cycle constitutes the value of SHM and is quantified with this framework. The approach is focused on fatigue deteriorating structural steel systems for which a continuous resistance deterioration formulation is introduced. In a case study, the value of SHM for load monitoring is calculated for a Daniels system subjected to fatigue deterioration. The influence of and the value of SHM in regard to the structural system risks and the integrity management is explicated and explained. The results are pointing to the importance of the consideration of the structural system risks for the quantification of the value of SHM.

On the value of SHM in the context of service life integrity management

ABSTRACT:This paper addresses the optimization of structural health monitoring(SHM) before its implementation on the basis of its Value of Information (VoI). The approach for the quantification of the value of SHM builds upon a service life cost assessment and generic structural performance model in conjunction with the observation, i.e. monitoring, of deterioration increments. The structural performance is described with a generic deterioration model to be calibrated to the relevant structural deterioration mechanism, such as e.g. fatigue and corrosion. The generic deterioration model allows for the incorporation of monitored damage increments and accounts for the precision of the data by considering the statistical uncertainties, i.e. the amount of monitoring data due to the monitoring period, and by considering the measurement uncertainty. The value of structural health monitoring is then quantified in the framework of the Bayesian pre-posterior decision theory as the difference between the expected service-life costs considering an optimal structural integrity management and the service life costs utilizing an optimal SHM system and structural integrity management. With an example the application of the approach is shown and the value of the monitoring period optimized SHM information is determined.

On the Value of Monitoring Information for the Structural Integrity and Risk Management

This article introduces an approach and framework for the quantification of the value of structural health monitoring (SHM) in the context of the structural risk and integrity management for systems. The quantification of the value of SHM builds upon the Bayesian decision and utility theory, which facilitates the assessment of the value of information associated with SHM. The principal approach for the quantification of the value of SHM is formulated by modeling the fundamental decision of performing SHM or not in conjunction with their expected utilities. The expected utilities are calculated accounting for the probabilistic performance of a system in conjunction with the associated structural integrity and risk management actions throughout the life cycle, the associated benefits, structural risks, and costs and when performing SHM, the SHM information, their probabilistic outcomes, and costs. The calculation of the expected utilities necessitates a comprehensive and rigorous modeling, which is introduced close to the original formulations and for which analysis characteristics and simplifications are described and derived. The framework provides the basis for the optimization of the structural risk and integrity management based on utility gains including or excluding SHM and inspection information. Studies of fatigue deteriorating structural Systems and their characteristics (1) provide decision Support for the performance of SHM, (2) explicate the influence of the structural component and system characteristics on the value of SHM, and (3) demonstrate how an integral optimization of SHM and inspection strategies for an efficient structural risk and integrity management can be performed.

Integrating structural health and condition monitoring: a cost benefit analysis for offshore wind energy

There is a large financial incentive to minimise operations and maintenance (O&M) costs for offshore wind power by optimising the maintenance plan. The integration of condition monitoring (CM) and structural health monitoring (SHM) may help realise this. There is limited work on the integration of both CM and SHM for offshore wind power or the use of imperfectly operating monitoring equipment. In order to investigate this, a dynamic Bayesian network and limit state equations are coupled with Monte Carlo simulations to deteriorate components in a wind farm.The CM system has a ‘deterioration window’ allowing for the possible detection of faults up to 6 months in advance. The SHM system model uses a reduction in the probability of failure factor to account for lower modelling uncertainties.A case study is produced that shows a reduction in operating costs and also a reduction in risk. The lifetime levelised costs are reduced by approximately 6%.

Condition monitoring benefit for operation support of offshore wind turbines

As more offshore wind parks are commissioned, the focus will inevitably shift from a planning, construction, and warranty focus to an operation, maintenance, and investment payback focus. In this latter case, both short-term risks associated with wind turbine component assemblies, and long-term risks related to integrity of the support structure, are highly important. This research focuses on the role of condition monitoring to lower costs and risks associated with short-term reliability and long-term asset integrity. This enables comparative estimates of the life cycle costs and reduction in uncertainty, both of which are of value to investors.

Introduction: The Value of Health Monitoring in Structural Performance Assessment

Structural health monitoring (SHM) has evolved over decades of continuous progress in measuring, processing, collecting and storing massive amounts of data that can provide valuable information for owners and managers in order to control and manage the integrity of their structures. The data sets acquired from SHM systems are undoubtedly of the “big data” type due to their sheer volume, complexity and diversity, and conducting relevant analyses of their content can help to identify damage or failure during operation through the relationships between the measurements taken by multiple sensors. A great deal can be learned from these large pools of data, resulting in significant advances in efficient integrity control. From banking to retail, many sectors have already embraced big data, which is often synonymous with “big expectations”; in the present case, it offers opportunities to apply dataprocessing research to the development of more efficient SHM systems with real-time capabilities. By presenting various examples of bridge monitoring systems, this paper contributes to the ongoing cross-disciplinary efforts in data science for the utilization and advancement of SHM.

Reliability Assessment of a Bridge Structure Subjected to Chloride Attack

Abstract Prediction of the service lifetime of concrete structures with respect to chloride ingress involves a number of parameters that are associated with large uncertainties. Hence, full-scale measurements are strongly in demand. This paper begins by summarizing statistical distributions based on measurements taken from the Gimsøystraumen Bridge in Norway. A large number of chloride profiles are available based on concrete coring samples, and for each of these profiles the diffusion coefficient and surface concentration (due to sea spray) are estimated. Extensive measurements of the concrete cover depth are also performed. The probability distributions are input into a prediction model for chloride concentration at the steel reinforcement. By also introducing the critical chloride concentration as a random variable, the probability of exceeding the critical threshold is determined as a function of time. To address chloride attack on the entire bridge, a system model with 90 components is introduced. Reliability updating based on observations at multiple sites along the bridge is also investigated. First-order reliability methods typically become inaccurate for large systems of this type, so an enhanced Monte Carlo simulation method is applied. It is shown that the corresponding computation time is significantly reduced compared to crude Monte Carlo methods.

On Damage Detection System Information for Structural Systems

Abstract Damage detection systems (DDSs) provide information on the integrity of structural systems in contrast to local information from inspections or non-destructive testing (NDT) techniques. In this paper, an approach is developed that utilizes DDS information to update structural system reliability and integrate this information into risk and decision analyses. The approach includes a novel performance modelling of DDSs accounting for the structural and measurement system characteristics, the damage detection algorithm (DDA) precision including type I and II errors. This DDS performance modelling provides the basis for DDS comparison and assessment in conjunction with the structural system performance including the damage and failure state dependencies. For updating of the structural system reliability, an approach is developed based on Bayesian updating facilitating the use of DDS information on structural system level and thus for a structural system risk analysis. The structural system risk analysis encompasses the static, dynamic, deterioration, reliability and consequence models, which provide the basis for calculating the direct risks due to component failure and the indirect risks due to system failure. Two case studies with the developed approach demonstrate a potential risk reduction and a high Value of DDS Information.

Fatigue Model for the Structural Integrity Evaluation Applied to a Wind Turbine Concrete Shaft, Considering Corrosion and Freeze and Thaw Degradation

Fatigue is one of the principal damage mechanisms in a slender concrete structure under cyclic loads. And needs to be calculated locally through all the structure, considering the lading conditions and the particularities of concrete. The model presented here is capable to account for the fatigue damage in a probabilistic way, relating the annual loading conditions for each point and the degradation processes with a probability of failure. The methodology is as well capable to model the effect of a repair and control the structural integrity using the monitored data.