Jannie Sønderkær Nielsen

Bio: Jannie Sønderkær Nielsen is an academic researcher from Aalborg University. The author has contributed to research in topics: Offshore wind power & Wind power. The author has an hindex of 7, co-authored 26 publications receiving 361 citations.

Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades

Abstract: To optimally plan maintenance of wind turbine blades, knowledge of the degradation processes and the remaining useful life is essential. In this paper, a method is proposed for calibration of a Markov deterioration model based on past inspection data for a range of blades, and updating of the model for a specific wind turbine blade, whenever information is available from inspections and/or condition monitoring. Dynamic Bayesian networks are used to obtain probabilities of inspection outcomes for a maximum likelihood estimation of the transition probabilities in the Markov model, and are used again when updating the model for a specific blade using observations. The method is illustrated using indicative data from a database containing data from inspections of wind turbine blades.

Methods for Risk-Based Planning of O&M of Wind Turbines

Abstract: In order to make wind energy more competitive, the big expenses for operation and maintenance must be reduced. Consistent decisions that minimize the expected costs can be made based on risk-based methods. Such methods have been implemented for maintenance planning for oil and gas structures, but for offshore wind turbines, the conditions are different and the methods need to be adjusted accordingly. This paper gives an overview of various approaches to solve the decision problem: methods with decision rules based on observed variables, a method with decision rules based on the probability of failure, a method based on limited memory influence diagrams and a method based on the partially observable Markov decision process. The methods with decision rules based on observed variables are easy to use, but can only take the most recent observation into account, when a decision is made. The other methods can take more information into account, and especially, the method based on the Markov decision process is very flexible and accurate. A case study shows that the Markov decision process and decision rules based on the probability of failure are equally good and give lower costs compared to decision rules based on observed variables.

Computational framework for risk-based planning of inspections, maintenance and condition monitoring using discrete Bayesian networks

Abstract: This paper presents a computational framework for risk-based planning of inspections and repairs for deteriorating components. Two distinct types of decision rules are used to model decisions: simp...

A case study on risk-based maintenance of wind turbine blades with structural health monitoring

Abstract: The paper presents a case study demonstrating the methods used to estimate the value of the information (VoI) delivered by a structural health monitoring (SHM) system. The monitored object is a bla...

Maintenance policy optimization—literature review and directions

Abstract: Numerous of maintenance policies were developed due to the change in the manufacturing environment and the growing of technologies in the past few decades. Due to fluctuation (oscillation, instability) phenomena of the manufacturing industry, it is difficult to identify an optimal maintenance policy that actually suit for a manufacturing system. Thus, a lot of efforts have been done in order to assist manufacturing industry in finding an optimal maintenance policy. This paper attempts to review past and current research on optimal maintenance policy selection issues associated with methods used as well as the applications. Published literatures were systematically classified based on certainty theory in operation management classification model in term of certainty, uncertainty, and risk. Furthermore, a sub family had been classified based on the approaches used in determining the optimal maintenance policy. The possible gap occurred between academic research and industrial application in maintenance policy optimization is also discussed in detail, and several possible ideas are put forward to reduce the gap. More importantly, the paper is intended to provide a different view on classifying these models and give useful references for personnel working in industrial as well as researchers.

Offshore wind turbine operations and maintenance: A state-of-the-art review

Abstract: Operations and maintenance of offshore wind turbines (OWTs) play an important role in the development of offshore wind farms. Compared with operations, maintenance is a critical element in the levelized cost of energy, given the practical constraints imposed by offshore operations and the relatively high costs. The effects of maintenance on the life cycle of an offshore wind farm are highly complex and uncertain. The selection of maintenance strategies influences the overall efficiency, profit margin, safety, and sustainability of offshore wind farms. For an offshore wind project, after a maintenance strategy is selected, schedule planning will be considered, which is an optimization problem. Onsite maintenance will involve complex marine operations whose efficiency and safety depend on practical factors. Moreover, negative environmental impacts due to offshore maintenance deserve attention. To address these issues, this paper reviews the state-of-the-art research on OWT maintenance, covering strategy selection, schedule optimization, onsite operations, repair, assessment criteria, recycling, and environmental concerns. Many methods are summarized and compared. Limitations in the research and shortcomings in industrial development of OWT operations and maintenance are described. Finally, promising areas are identified with regard to future studies of maintenance strategies.