Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

생체의 모델링과 Simulation

Degradation models have become an important analytic tool for complex systems. During the last two decades, a number of degradation models have been developed to capture the degradation dynamics of a system and aid the subsequent decision-makings. This paper is aimed at providing a summary of the state of the arts in the field, and discussing some further research issues from both analytical and practical point of view. In this paper, degradation models are classified into three classes, that is, stochastic process models, general path models, and other models beyond these two classes. A review on the three classes is given with emphasis on the class of stochastic process models. A comprehensive comparison between stochastic process models and general path models is given to expound the pros and cons of these two methods. Applications of degradation models in degradation test planning and burn-in modelling will also be discussed. Copyright © 2014 John Wiley & Sons, Ltd.

Stochastic modelling and analysis of degradation for highly reliable products

Determining and understanding offshore wind turbine failure rates and resource requirement for repair are vital for modelling and reducing O&M costs and in turn reducing the cost of energy. While few offshore failure rates have been published in the past even less details on resource requirement for repair exist in the public domain. Based on ~350 offshore wind turbines throughout Europe this paper provides failure rates for the overall wind turbine and its sub-assemblies. It also provides failure rates by year of operation, cost category and failure modes for the components/sub-assemblies that are the highest contributor to the overall failure rate. Repair times, average repair costs and average number of technicians required for repair are also detailed in this paper. An onshore to offshore failure rate comparison is carried out for generators and converters based on this analysis and an analysis carried out in a past publication. The results of this paper will contribute to offshore wind O&M cost and resource modelling and aid in better decision making for O&M planners and managers.

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Failure rate, repair time and unscheduled O&M cost analysis of offshore wind turbines

Advanced logistics planning for offshore wind farm operation and maintenance activities

Different configurations of gearbox, generator and power converter exist for offshore wind turbines. This paper investigated the performance of four prominent drive train configurations over a range of sites distinguished by their distance to shore. Failure rate data from onshore and offshore wind turbine populations was used where available or systematically estimated where no data was available. This was inputted along with repair resource requirements to an offshore accessibility and operation and maintenance model to calculate availability and operation and maintenance costs for a baseline wind farm consisting of 100 turbines. The results predicted that turbines with a permanent magnet generator and a fully rated power converter will have a higher availability and lower operation and maintenance costs than turbines with doubly-fed induction generators. This held true for all sites in this analysis. It was also predicted that in turbines with a permanent magnet generator, the direct drive configuration has the highest availability and lowest operation and maintenance costs followed by the turbines with 2 stage and 3 stage gearboxes.

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Availability, operation and maintenance costs of offshore wind turbines with different drive train configurations

Development of a combined operational and strategic decision support model for offshore wind

Motivated by an industrial problem affecting a water utility, we develop a model for a load sharing system where an operator dispatches work load to components in a manner that manages their degradation. We assume degradation is the dominant failure type, and that the system will not be subject to sudden failure due to a shock. By deriving the time to degradation failure of the system, estimates of system probability of failure are generated, and optimal designs can be obtained to minimize the long run average cost of a future system. The model can be used to support asset maintenance and design decisions. Our model is developed under a common set of core assumptions. That is, the operator allocates work to balance the level of the degradation condition of all components to achieve system performance. A system is assumed to be replaced when the cumulative work load reaches some random threshold. We adopt cumulative work load as the measure of total usage because it represents the primary cause of component degradation. We model the cumulative work load of the system as a monotone increasing and stationary stochastic process. The cumulative work load to degradation failure of a component is assumed to be inverse Gaussian distributed. An example, informed by an industry problem, is presented to illustrate the application of the model under different operating scenarios.

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A load sharing system reliability model with managed component degradation

Uncertainty analysis has become an increasingly important part of risk assessments and operations research models, and the role of expert judgment in providing information for decision making has become more useful. Decision makers often have access to more than one expert, and it is common to make the decisions on the basis of the expertise of several experts, which leads to the problem of how to combine or aggregate the experts' judgments. A number of approaches have been proposed as to how to elicit, and how to synthesize, the different experts' knowledge. We discuss a variety of models that lead to specific combination methods. The output of these methods is a “combined probability distribution,” which can be viewed as representing a summary of the current state of information regarding the uncertainty of interest. We also briefly review the psychology literature relating to group decision making, which, obviously, is relevant to behavioral aggregation.

Matthew Revie

Papers

Advanced logistics planning for offshore wind farm operation and maintenance activities

Availability, operation and maintenance costs of offshore wind turbines with different drive train configurations

Development of a combined operational and strategic decision support model for offshore wind

A load sharing system reliability model with managed component degradation

Eliciting subjective probability distributions from groups