Approximate Bayesian Computation as a New Tool for Partial Discharge Analysis of Partial Discharge Data
Partial Discharges are short breakdowns inside electrical equipment. As they indicate weaknesses of the insulation strength, they are seen as important precursors to a failure of the system. Therefore measurement and analysis of the patterns of instances in time and strength of the discharge are an important tool to analyze the insulation status of electric equipment, that has been addressed already using different methods in the past. In this work we explore how a physics-based stochastic process can be combined with Approximate Bayesian Computation (ABC) as a new way to analyze them. ABC is a method to infer probability distributions of model parameters in cases, where the likelihood is not tractable, but simulations can be done easily. As such it is of interest for complex phenomena or measurement systems, as often found in prognostics applications. Especially the ABC-SMC method was found to be useful here. Real Partial Discharge measurement data was used not only for parameter estimation, but also to do model comparison in order to compare different physical models proposed in the literature.
How to Cite
Stochastic process, Approximate Bayesian Computation, Prognostics
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.