Structural Health Assessment through Vibration Monitoring on FPSOs

In the offshore oil and gas industry, production takes place at more and more remote locations, with Floating Production Storage Offloading units (FPSO’s) often being selected for field development. These units may remain on station during their entire lifetime while operating under adverse weather conditions. Inspections, which have to be performed on site, are therefore becoming a challenging and risky operation. Within this context, various Structural Health Monitoring (SHM) schemes are being explored in an attempt to ensure integrity of offshore units. The goal of this project is to study the feasibility of structural health assessment on FPSOs using vibration-based monitoring techniques, with the ultimate aim to minimize inspections of confined spaces. To this end, a typical panel structure on a FPSO hull is considered and modelled using the Finite Element (FE) method. As part of a ballast tank, the considered component is inevitably subject to structural degradation, with corrosion and fatigue cracks constituting the main degradation mechanisms. The constructed FE model is therefore appropriately parametrized in order to accommodate the simulation of the aforementioned damage conditions. The first part of this study, referred to as the forward problem, consists of modelling the dominant degradation mechanisms experienced by hull structures of FPSO’s, namely uniform corrosion, pitting corrosion and fatigue cracks. These are introduced with varying degrees of deterioration into the reference FE model of the said stiffened panel and the sensitivity of the vibrational characteristics, i.e. the natural frequencies, mode shapes and damping ratios, to these changes is investigated. The aim of this part is to extract the identifiable damage scenarios that will serve as the basis for the structural health assessment through the implementation of Operational Modal Analysis (OMA). In the second part of the study, the so-called inverse process, the stiffened panel is assumed to be monitored during normal operation using a conventional monitoring system (i.e. accelerometers). The latter is configured in such a way that observability of all modes is accomplished and robustness of the identified properties is ensured. Excitation of the structure is assumed to be sloshing-induced impulsive loads and the measured noisy signals are processed with a set of Stochastic Subspace Identification (SSI) algorithms, upon enhancement with a cluster analysis in order to enable automatic system identification. For each one of the damage scenarios, the dynamic properties are identified and cross-compared with those of the reference model. The feasibility of damage detection through vibration monitoring, along with the existing restrictions, is finally determined and a possible extension of the proposed formulation is discussed.Civil Engineering and GeosciencesHydraulic EngineeringOffshore and Dredging Engineerin

A comparison of two Kalman-type filters for robust extrapolation of offshore wind turbine support structure response

Quasi-periodic loading resulting from waves and a rotationally sampled wind field often leads to fatiguedriven designs for offshore wind turbine support structures. The uncertainty on wind and wave loading, together with large modelling uncertainties, lead to large discrepancies between the observed and predicted dynamic behaviour of these structures. Among many recent-developed techniques for monitoring of true fatigue damage development, two promising Kalman-type filters are compared, namely the recently proposed Dual Kalman filter (DKF) and the Gillijns and De Moor filter (GDF). The filters are applied to synthetic vibration data in order to predict the global response of a lattice support structure assuming large modelling uncertainties and no knowledge of the input forces. A critical assessment of both filterswith regard to requirements on the available data and tuning of the filter parameters is presented.Offshore Engineerin

Reliability prediction of fatigue damage accumulation on wind turbine support structures

The evaluation of fatigue damage accumulation on wind turbine support structures under operational conditions is heavily influenced by a number of uncertainties. These uncertainties may, firstly, be attributed to the highly variable and complex environmental loads, and secondly, to the unavoidable modelling errors which mainly originate from the inherent randomness in both material properties and fatigue resistance of structural components. It is therefore essential that assessment of fatigue life is carried out within a probabilistic framework; one that accounts for the stochastic nature of the phenomenon. The present study proposes a strategy for real-time reliability prediction of accumulated fatigue damage on wind turbine support structures by taking into account the above-mentioned uncertainties. To this end, the availability of structural monitoring information for the identification of the global response on wind-turbine support structures is exploited in order to address the discrepancies between actual and predicted damage accumulation. This is carried through utilization of an augmented version of the Kalman filter, which is capable of jointly estimating the response and the unknown inputs of the structure while relying on a limited number of noisy observations and a presumably uncertain model of the real system. A fixed-lag smoother is further deployed for the attenuation of the estimation error in an on-line mode and the smoothed stochastic estimates of the response are propagated over the model at the level of stresses. The accumulated damage along with the corresponding reliability level is finally predicted using a stochastic nonstationary fatigue damage model. The proposed scheme is demonstrated via implementation on the NREL 5.0 MW wind turbine under different operational conditions, on the basis of dummy vibration data generated via the FAST software.Offshore Engineerin

A general substructure-based framework for input-state estimation using limited output measurements

This paper presents a general framework for estimating the state and unknown inputs at the level of a system subdomain using a limited number of output measurements, enabling thus the component-based vibration monitoring or control and providing a novel approach to model updating and hybrid testing applications. Under the premise that the system subdomain dynamics are driven by the unknown (i) externally applied inputs and (ii) interface forces, with the latter representing the unmodeled system components, the problem of output-only response prediction at the substructure level can be tailored to a Bayesian input-state estimation context. As such, the solution is recursively obtained by fusing a Reduced Order Model (ROM) of the structural subdomain of interest with the available response measurements via a Bayesian filter. The proposed framework is without loss of generality established on the basis of fixed- and free-interface domain decomposition methods and verified by means of three simulated Wind Turbine (WT) structure applications of increasing complexity. The performance is assessed in terms of the achieved accuracy on the estimated unknown quantities.Offshore EngineeringDynamics of Structure