Failure Estimation of Offshore Renewable Energy Devices Based on Hierarchical Bayesian Approach

Abstract

Improving the reliability of marine renewable energy devices such as wave and tidal energy convertors is an important task, primarily to minimize the perceived risks and reduce the associated cost for operation and maintenance. Marine systems involve a wide range of uncertainties, due to the complexity of failure mechanism of the marine components, scarcity of data, human interactions and randomness of the sea environment. The fundamental element of a probabilistic risk analysis necessarily needs to rely on operational information and observation data to quantify the performance of the system. However, in reality it is difficult to ascertain observation of the precursor data according to the number of component failures that have occurred, mainly as a result of imprecision in the failure criterion, record keeping, or experimental and physical modelling of the process. Traditional reliability estimation approaches such as Fault Tree, Event Tree and Reliability Block Diagram analysis offer simplified, rarely realistic models of this complex reliability problem. The main reason is that they all rely on accurate prior information as a perquisite for performing reliability assessment. In this paper, a hierarchical Bayesian framework is developed for modelling marine renewable component failures encountered the uncertainty. The proposed approach is capable to incorporate the conditions, which lack reliable observation data (e.g. unknown/uncertain failure rate of a component). The hierarchical Bayesian framework provides a platform for the propagation of uncertainties through the reliability assessment of the system, via Markov Chain Monte Carlo (MCMC) sampling. The advantages of using MCMC sampling has proliferated Bayesian inference for conducting risk and reliability assessment of engineering system. It is able to use hyper-priors to represent prior parameters as a subjective observations for probability estimation of the failure events and enable an updating process for quantitative reasoning of interdependence between parameters. The developed framework will be an assistive tool for a better monitoring of the operation in terms of evaluating performance of marine renewable system under the risk of failure. The paper illustrates the approach using a tidal energy convertor as a case study for estimating components failure rates and representing the uncertainties of system reliability. The paper will be of interest to reliability practitioners and researchers, as well as tidal energy technology and project developers, seeking a more accurate reliability estimation framework.

A Bayesian hierarchical assessment of night shift working for offshore wind farms

“…Abaei et al 31 use a hierarchical Bayesian framework to assess the reliability of a modular tide generator. They argue that the method's capability to handle uncertainties robustly has the potential to ‘minimize perceived risks and reduce the associated cost for operation and maintenance’.…”