Diagnostics and prognostics utilising dynamic Bayesian networks applied to a wind turbine gearbox

The UK has the largest installed capacity of offshore wind and this is set to increase significantly in future years. The difficulty in conducting maintenance offshore leads to increased operation and maintenance costs compared to onshore but with better condition monitoring and preventative maintenance strategies these costs could be reduced. In this paper an on-line condition monitoring system is created that is capable of diagnosing machine component conditions based on an array of sensor readings. It then informs the operator of actions required. This simplifies the role of the operator and the actions required can be optimised within the program to minimise costs. The program has been applied to a gearbox oil testbed to demonstrate its operational suitability. In addition a method for determining the most cost effective maintenance strategy is examined. This method uses a Dynamic Bayesian Network to simulate the degradation of wind turbine components, effectively acting as a prognostics tool, and calculates the cost of various preventative maintenance strategies compared to purely corrective maintenance actions. These methods are shown to reduce the cost of operating wind turbines in the offshore environment

Modelling the effect of maintenance strategies and reliability for long-term wind yield assessment

Where a number of onshore wind farm locations are being maintained by a single central Operation and Maintenance Contractor, an effective competition exists between those sites for the use of that maintenance resource. Any differentials between those sites in terms of the costs of repair to the contractor, or the potential return to the contractor for improving the site availability under the Operations and Maintenance Contract, may mean a variance in the level of operational availability achieved by each site. A review of UK contract terms illustrates the potential differentials that may occur. A maintenance optimisation model is created which is used to simulate the potential availabilities of a set of wind farms maintained from a central resource in response to typical published failure rates and restoration times. Monte Carlo methods are applied to this simulation to provide an illustrative set of sensitivities which may be used to adjust availability losses assumed during the energy yield analysis of a potential onshore wind farm location

Effects of Chloride ions on Carbonation Rate of Hardened Cement Paste by X-ray CT Techniques

Corrosion of steel bars in concrete structures is initiated as a result of concrete carbonation and/or chloride intrusion, and influenced by their interaction. This paper presents an experimental investigation into the effect of chloride ions on carbonation of cement paste by means of X-ray CT techniques and mercury intrusion porosimetry(MIP), which is benchmarked by the conventional phenolphthalein method. A group of the cement paste cylinders with different amounts of chlorides ions were manufactured and cured before they were subjected to an accelerated carbonation process in a conditional cabinet regime for different ages. The carbonation front of the cement paste was first evaluated using phenolphthalein method. This was followed by an investigation of microstructure evolution of the cement paste using XCT and MIP techniques. The experimental results show that the carbonation of a cement paste increases with its water to cement ratio and with carbonation ages, but decrease with its amount of chloride ions. In particular, it has been found that increases of chloride ion of a cement paste refine its porous structures, decrease its porosity and eventually mitigate its carbonation rate. The relevant results can be referred to for durability design and prediction of reinforced concrete structures

Modelling epistemic uncertainty in offshore wind farm production capacity to reduce risk

Financial stakeholders in offshore wind farm projects require predictions of energy production capacity to better manage the risk associated with investment decisions prior to construction. Predictions for early operating life are particularly important due to the dual effects of cash flow discounting and the anticipated performance growth due to experiential learning. We develop a general marked point process model for the times to failure and restoration events of farm subassemblies to capture key uncertainties affecting performance. Sources of epistemic uncertainty are identified in design and manufacturing effectiveness. The model then captures the temporal effects of epistemic and aleatory uncertainties across subassemblies to predict the farm availability‐informed relative capacity (maximum generating capacity given the technical state of the equipment). This performance measure enables technical performance uncertainties to be linked to the cost of energy generation. The general modeling approach is contextualized and illustrated for a prospective offshore wind farm. The production capacity uncertainties can be decomposed to assess the contribution of epistemic uncertainty allowing the value of gathering information to reduce risk to be examined

Using the Value of Information to improve conservation decision making.

Conservation decisions are challenging, not only because they often involve difficult conflicts among outcomes that people value, but because our understanding of the natural world and our effects on it is fraught with uncertainty. Value of Information (VoI) methods provide an approach for understanding and managing uncertainty from the standpoint of the decision maker. These methods are commonly used in other fields (e.g. economics, public health) and are increasingly used in biodiversity conservation. This decision-analytical approach can identify the best management alternative to select where the effectiveness of interventions is uncertain, and can help to decide when to act and when to delay action until after further research. We review the use of VoI in the environmental domain, reflect on the need for greater uptake of VoI, particularly for strategic conservation planning, and suggest promising areas for new research. We also suggest common reporting standards as a means of increasing the leverage of this powerful tool. The environmental science, ecology and biodiversity categories of the Web of Knowledge were searched using the terms 'Value of Information,' 'Expected Value of Perfect Information,' and the abbreviation 'EVPI.' Google Scholar was searched with the same terms, and additionally the terms decision and biology, biodiversity conservation, fish, or ecology. We identified 1225 papers from these searches. Included studies were limited to those that showed an application of VoI in biodiversity conservation rather than simply describing the method. All examples of use of VOI were summarised regarding the application of VoI, the management objectives, the uncertainties, the models used, how the objectives were measured, and the type of VoI. While the use of VoI appears to be on the increase in biodiversity conservation, the reporting of results is highly variable, which can make it difficult to understand the decision context and which uncertainties were considered. Moreover, it was unclear if, and how, the papers informed management and policy interventions, which is why we suggest a range of reporting standards that would aid the use of VoI. The use of VoI in conservation settings is at an early stage. There are opportunities for broader applications, not only for species-focussed management problems, but also for setting local or global research priorities for biodiversity conservation, making funding decisions, or designing or improving protected area networks and management. The long-term benefits of applying VoI methods to biodiversity conservation include a more structured and decision-focused allocation of resources to research

Maintenance Optimization and Inspection Planning of Wind Energy Assets: Models, Methods and Strategies

Designing cost-effective inspection and maintenance programmes for wind energy farms is a complex task involving a high degree of uncertainty due to diversity of assets and their corresponding damage mechanisms and failure modes, weather-dependent transport conditions, unpredictable spare parts demand, insufficient space or poor accessibility for maintenance and repair, limited availability of resources in terms of equipment and skilled manpower, etc. In recent years, maintenance optimization has attracted the attention of many researchers and practitioners from various sectors of the wind energy industry, including manufacturers, component suppliers, maintenance contractors and others. In this paper, we propose a conceptual classification framework for the available literature on maintenance policy optimization and inspection planning of wind energy systems and structures (turbines, foundations, power cables and electrical substations). The developed framework addresses a wide range of theoretical and practical issues, including the models, methods, and the strategies employed to optimise maintenance decisions and inspection procedures in wind farms. The literature published to date on the subject of this article is critically reviewed and several research gaps are identified. Moreover, the available studies are systematically classified using different criteria and some research directions of potential interest to operational researchers are highlighted

Quantification and modelling of epistemic uncertainties for availability risk of future offshore wind farms using expert judgement

We develop a model to capture state-of-knowledge, as well as aleatory, uncertainties associated with off-shore wind farm technologies, processes and environments. Our goal is to better understand systemic technology risks and support investment decisions for effective, efficient risk management. Typical epistemic uncertainties present in the offshore wind context are articulated. A protocol for eliciting expert judgment to quantify variables representing epistemic uncertainties and other relevant model parameters is presented. We discuss the elicitation of judgments from an expert panel of energy company technical specialists and show an application of our model to a generic new design offshore wind farm

Exploring a Bayesian approach for structural modelling of common cause failures

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Exploring a Bayesian approach for structural modelling of common cause failures

Common Cause Failures (CCFs) are a class of dependent failures that occur to complex technological systems, such as nuclear power plants, where redundant components serve as multiple layers of defence. For the purposes of quantitative assessment of CCFs, parametric models are used. A common feature of all parametric models is the difficulty in parameter estimation due to limited available observational data.The Unified Partial Method (UPM) for CCF modelling is a systematic methodology that takes into consideration physical and operational system defences. This research explores the application of the Influence Diagram (ID) formalism in order to extend UPM, through an example of Emergency Diesel Generators from nuclear power plants. The proposed model incorporates intermediate stages in the modelling process, namely root causes and coupling factors, to allow for a representation of the CCF mechanisms. Moreover, it captures interactions existing amongst the system's defences, in their contribution to risk. With an underlying Bayesian approach to risk, the model quantifies operational experience, accounts for the epistemic uncertainty, and allows for a coherent combination of expert opinion with observations. This thesis proposes a model structure, which integrates with the ICDE generic database for CCFs. Finally, the ID formalism allows for the propagation of uncertainty within the model structure, and provides a tool for decision-making.The construction of the ID model has been entirely based on expert judgment: the model network has been constructed with the help of experts, whilst a suggested model quantification methodology has been explored. This thesis documents the building process, and explores the behaviour of the resulting model. Findings within this research suggest the feasibility of the proposed methodology for development of a CCF model with a structural and exploratory character.Common Cause Failures (CCFs) are a class of dependent failures that occur to complex technological systems, such as nuclear power plants, where redundant components serve as multiple layers of defence. For the purposes of quantitative assessment of CCFs, parametric models are used. A common feature of all parametric models is the difficulty in parameter estimation due to limited available observational data.The Unified Partial Method (UPM) for CCF modelling is a systematic methodology that takes into consideration physical and operational system defences. This research explores the application of the Influence Diagram (ID) formalism in order to extend UPM, through an example of Emergency Diesel Generators from nuclear power plants. The proposed model incorporates intermediate stages in the modelling process, namely root causes and coupling factors, to allow for a representation of the CCF mechanisms. Moreover, it captures interactions existing amongst the system's defences, in their contribution to risk. With an underlying Bayesian approach to risk, the model quantifies operational experience, accounts for the epistemic uncertainty, and allows for a coherent combination of expert opinion with observations. This thesis proposes a model structure, which integrates with the ICDE generic database for CCFs. Finally, the ID formalism allows for the propagation of uncertainty within the model structure, and provides a tool for decision-making.The construction of the ID model has been entirely based on expert judgment: the model network has been constructed with the help of experts, whilst a suggested model quantification methodology has been explored. This thesis documents the building process, and explores the behaviour of the resulting model. Findings within this research suggest the feasibility of the proposed methodology for development of a CCF model with a structural and exploratory character

A location-specific CCF model for supporting the system design process

This paper examines a location-specific CCF model for supporting the system design process