A Bayesian Optimal Design for Accelerated Degradation Testing Based on the Inverse Gaussian Process

Accelerated degradation testing (ADT) is commonly used to obtain degradation data of products by exerting loads over usage conditions. Such data can be used for estimating component lifetime and reliability under usage conditions. The design of ADT entails to establish a model of the degradation process and define the test plan to satisfy given criteria under the constraint of limited test resources. Bayesian optimal design is a method of decision theory under uncertainty, which uses historical data and expert information to find the optimal test plan. Different expected utility functions can be selected as objectives. This paper presents a method for Bayesian optimal design of ADT, based on the inverse Gaussian process and considering three objectives for the optimization: Relative entropy, quadratic loss function, and Bayesian D-optimality. The Markov chain Monte Carlo and the surface fitting methods are used to obtain the optimal plan. By sensitivity analysis and a proposed efficiency factor, the Bayesian D-optimality is identified as the most robust and appropriate objective for Bayesian optimization of ADT

Decommissioning strategy to reduce the cost and risk-driving factors in the offshore wind industry.

With the increasing number of wind turbines approaching their end of life, there has to be a decommissioning strategy in place as the removal of these assets is not as direct as reverse installation. Offshore asset decommissioning involves technical, financial, operational, safety, policy, and environmental considerations on handling offshore marine assets at their end-of-life, with phases from the planning to site clean-up and monitoring. Offshore decommissioning activities cost significantly more than onshore; thus, adequate financial and safety provisions are essential, and more research required in this area. Decommissioning projects have hitherto been performed on a small scale, but with large-scale aging structures, they must be optimised for lowered costs and risks. In terms of planning, execution and costs, there have been significant cost overruns on decommissioning projects, which are not profit-generating projects. These forecasted large-scale decommissioning activities also have associated risks. Although risk management is a well-researched area, there is limited literature on offshore wind decommissioning risk management. This research thus, applies risk management methods and strategies to develop a robust decommissioning risk framework. In addition, to improve decommissioning processes and technologies, there is a need to develop new protocols for decommissioning. This research identifies potentials for computational simulations and automations that need to be developed to identify and manage the highest cost and risk-drivers. This study seeks to close the research gap in understanding how to decrease decommissioning costs and risks. This research addresses potential opportunities in cost and risk estimation research, impact analysis and reduction frameworks that can be adapted to decommissioning activities specific to the offshore wind industry.Shafiee, Mahmood (Associate)PhD in Energy and Powe