Decentralized Approach for Fault Diagnosis of Three Cell Converters
In this paper, an approach for fault diagnosis of hybrid dynamic systems (HDS), in particular discretely controlled continuous system, is proposed. The goal is to construct a decentralized diagnosis structure, able to diagnose parametric and discrete faults. This approach considers the system as composed of a set of interacted hybrid components (HCs). Each HC is composed of a discrete component (Dc), e.g. on/off switches, with the continuous components (Ccs), e.g. capacitors, whose continuous dynamic behavior is influenced by the Dc discrete states. A local hybrid diagnosis module, called diagnoser, is associated to each HC in order to diagnose the faults occurring in this HC. In order to take into account the interactions between the different HCs, local diagnosis decisions are merged using a coordinator. The latter issues a final decision about the origin of the fault and identifies its parameters. The advantage of the proposed approach is that local hybrid diagnosers as well as the coordinator are built using local models. The proposed approach is applied to achieve the decentralized diagnosis of discrete and parametric faults of power electronic three-cell converters.
How to Cite
Fault diagnosis, Hybrid Dynamic Systems
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.