State of the art in structural health monitoring of offshore and marine structures

This paper deals with state of the art in structural health monitoring (SHM) methods in offshore and marine structures. Most SHM methods have been developed for onshore infrastructures. Few studies are available to implement SHM technologies in offshore and marine structures. This paper aims to fill this gap and highlight the challenges in implementing SHM methods in offshore and marine structures. The present work categorises the available techniques for establishing SHM models in oil rigs, offshore wind turbine structures, subsea systems, vessels, pipelines and so on. Additionally, the capabilities of proposed ideas in recent publications are classified into three main categories: model-based methods, vibration-based methods and digital twin methods. Recently developed novel signal processing and machine learning algorithms are reviewed and their abilities are discussed. Developed methods in vision-based and population-based approaches are also presented and discussed. The aim of this paper is to provide guidelines for selecting and establishing SHM in offshore and marine structures.publishedVersio

[Report of] Specialist Committee V.4: ocean, wind and wave energy utilization

The committee's mandate was :Concern for structural design of ocean energy utilization devices, such as offshore wind turbines, support structures and fixed or floating wave and tidal energy converters. Attention shall be given to the interaction between the load and the structural response and shall include due consideration of the stochastic nature of the waves, current and wind

On the identification and parametric modelling of offshore dynamic systems

This thesis describes an investigation into the analysis methods arising from identification aspects of the theory of dynamic systems with application to full-scale offshore monitoring and marine environmental data including target spectra. Based on the input and output of the dynamic system, the System Identification (SI) techniques are used first to identify the model type and then to estimate the model parameters. This work also gives an understanding of how to obtain a meaningful matching between the target (power spectra or time series data sets) and SI models with minimal loss of information. The SI techniques, namely. Autoregressive (AR), Moving Average (MA) and Autoregressive Moving Average (ARMA) algorithms are formulated in the frequency domain and also in the time domain. The above models can only be economically applicable provided the model order is low in the sense that it is computationally efficient and the lower order model can most appropriately represent the offshore time series records or the target spectra. For this purpose, the orders of the above SI models are optimally selected by Least Squares Error, Akaike Information Criterion and Minimum Description Length methods. A novel model order reduction technique is established to obtain the reduced order ARMA model. At first estimations of higher order AR coefficients are determined using modified Yule-Walker equations and then the first and second order real modes and their energies are determined. Considering only the higher energy modes, the AR part of the reduced order ARMA model is obtained. The MA part of the reduced order ARMA model is determined based on partial fraction and recursive methods. This model order reduction technique can remove the spurious noise modes which are present in the time series data. Therefore, firstly using an initial optimal AR model and then a model order reduction technique, the time series data or target spectrum can be reduced to a few parameters which are the coefficients of the reduced order ARMA model. The above univariate SI models and model order reduction techniques are successfully applied for marine environmental and structural monitoring data, including ocean waves, semi-submersible heave motions, monohull crane vessel motions and theoretical (Pierson- Moskowitz and JONSWAP) spectra. Univariate SI models are developed based on the assumption that the offshore dynamic systems are stationary random processes. For nonstationary processes, such as, measurements of combined sea waves and swells, or coupled responses of offshore structures with short period and long period motions, the time series are modelled by the Autoregressive Integrated Moving Average algorithms. The multivariate autoregressive (MAR) algorithm is developed to reduce the time series wave data sets into MAR model parameters. The MAR algorithms are described by feedback weighting coefficients matrices and the driving noise vector. These are obtained based on the estimation of the partial correlation of the time series data sets. Here the appropriate model order is selected based on auto and cross correlations and multivariate Akaike information criterion methods. These algorithms are applied to estimate MAR power spectral density spectra and then phase and coherence spectra of two time series wave data sets collected at a North Sea location. The estimation of MAR power spectral densities are compared with spectral estimates computed from a two variable fast Fourier transform, which show good agreement

Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme

Advancing probabilistic risk assessment of offshore wind turbines on monopiles

Offshore Wind Turbines (OWTs) are a unique type of engineered structure. Their design spans all engineering disciplines, ranging from structural engineering for the substructure and foundation to electrical or mechanical engineering for the generating equipment. Consequently, the different components of an OWT are commonly designed independently using codified standards. Within the OWT design process, financial cost plays an important role as a constraint on decision making, because of the competition between prospective wind farm operators and with other forms of electricity generation. However, the current, independent design process does not allow for a combined assessment of OWT system financial loss. Nor does it allow for quantification of the uncertainties (e.g., wind and wave loading, materials properties) that characterise an OWT’s operations and which may have a strong impact on decision making. This thesis proposes quantifying financial losses associated with an OWT exposed to stochastic wind and wave conditions using a probabilistic risk modelling framework, as a first step towards evaluating Offshore Wind Farm (OWF) resilience. The proposed modelling framework includes a number of novel elements, including the development of site-specific fragility functions (relationships between the likelihood of different levels of damage experienced by an OWT over a range of hazard intensities), which account for uncertainties in both structural capacity and demands. As a further element of novelty, fragility functions are implemented in a closed-form assessment of financial loss, based on a combinatorial system reliability approach, which considers both structural and non-structural components. Two important structural performance objectives (or limit states) are evaluated in this thesis: 1) the Ultimate Limit State (ULS) which assesses the collapse of an OWT due to extreme wind and wave conditions, such as those resulting from hurricanes; and 2) the Fatigue Limit State (FLS), which addresses the cumulative effects of operational loading, i.e., cracks growing over the life of the structure until they threaten its integrity. This latter limit state is assessed using a novel machine learning technique, Gaussian Process (GP) regression, to develop a computationally-efficient surrogate model that emulates the output from computationally-expensive time-domain structural analyses. The consequence of the OWT failing is evaluated by computing annualised financial losses for the full OWT system. This provides a metric which is easily communicable to project stakeholders, and can also be used to compare the relative importance of different components and design strategies. Illustrative applications at case-study sites are presented as a walk-through of the calculation steps in the proposed framework and its various components. The calculation of losses provides a foundation from which a more detailed assessment of OWT and OWF resilience could be developed

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area

Adaptive Simulation Modelling Using The Digital Twin Paradigm

Structural Health Monitoring (SHM) involves the application of qualified standards, by competent people, using appropriate processes and procedures throughout the struc- ture’s life cycle, from design to decommissioning. The main goal is to ensure that through an ongoing process of risk management, the structure’s continued fitness-for-purpose (FFP) is maintained – allowing for optimal use of the structure with a minimal chance of downtime and catastrophic failure. While undertaking the SHM task, engineers use model(s) to predict the risk to the structure from degradation mechanisms such as corrosion and cracking. These predictive models are either physics-based, data-driven or hybrid based. The process of building these predictive models tends to involve processing some input parameters related to the material properties (e.g.: mass density, modulus of elasticity, polarisation current curve, etc) or/and the environment, to calibrate the model and using them for the predictive simulation. So, the accuracy of the predictions is very much dependent upon the input data describing the properties of the materials and/or the environmental conditions the structure experiences. For the structure(s) with non-uniform and complex degradation behaviour, this pro- cess is repeated over the life-time of the structure(s), i.e., when each new survey is per- formed (or new data is available) and then the survey data are used to infer changes in the material or environmental properties. This conventional parameter tuning and updat- ing approach is computationally expensive and time-consuming, as multi-simulations are needed and manual intervention is expected to determine the optimal model parameters. There is therefore a need for a fundamental paradigm shift to address the shortcomings of conventional approaches. The Digital Twin (DT) offers such a paradigm shift in that it integrates ultra-high fidelity simulation model(s) with other related structural data, to mirror the structural behaviour of its corresponding physical twin. DT’s inherent ability to handle large data allows for the inclusion of an evolving set of data relating to the struc- ture with time as well as provides for the adaptation of the simulation model with very little need for human intervention. This research project investigated DT as an alternative to the existing model calibration and adaptation approach. It developed a design of experiment platform for online model validation and adaptation (i.e., parameter updating) solver(s) within the Digital Twin paradigm. The design of experimental platform provided a basis upon which an approach based on the creation of surrogates and reduced order model (ROM)-assisted parameter search were developed for improving the efficiency of model calibration and adaptation. Furthermore, the developed approach formed a basis for developing solvers which pro- vide for the self-calibration and self-adaptation capability required for the prediction and analysis of an asset’s structural behaviour over time. The research successfully demonstrated that such solvers can be used to efficiently calibrate ultra-high-fidelity simulation model within a DT environment for the accurate prediction of the status of a real-world engineering structure

Stress Estimation of Offshore Structures

Offshore structures are subjected to a harsh environment where the fluctuating waves continuously strain the structures and these forces cause the initiation and propagation of cracks in the structures. In other words, the structures accumulate fatigue damage, which eventually leads to structural failure. To avoid fatigue failure, the operational lifetime of a structure is limited to a design lifetime in which the structure is safe for operation. This design process is based on precautious stochastic assessments, norms, and industry standards that simplify the actual structure and environment in such a manner that it involves little risk of structural failure. As many structures in the North Sea approach the end of their design lifetime, the owners are faced with a dilemma: either abandon the field or replace the structures. Another option is the lifetime extension of the existing structures. This requires a reduction of the uncertainties in the design process - such as the stress history in fatigue-critical location. Unfortunately, these locations are often inaccessible or directly harmful to the sensors due to the hostile environment of the ocean. This thesis focuses on virtual sensing to estimate the stress/strain response of offshore structures by indirect measurements. The thesis addresses the state of the art and maps some essential issues within stress/strain estimation. In this thesis, stress/strain estimation is applied to different test specimens to address certain scientific issues. Parts of the thesis relate to the calibration of the system model for virtual sensing by operational modal analysis