One of the major challenges in model based fault detection is the robust design of thresholds for the analytical redundancy relations. Those relations are residuals which differ from a zero in case of a fault and are equal to zero in the fault free case. In real world applications, however, these residuals usually differ from zero even in the fault free case due to, e.g. measurement errors and model uncertainties. This paper proposes a method based on Monte-Carlo simulations of possible residuals taking into account uncertainties as a-priori probability distributions. The statistical analysis of the resulting residual's probability distribution using posterior highest density regions enables a likelihood-based decision about the occurrence of a fault. The presented method is demonstrated and evaluated using a nonlinear physical model of an air cooling system of an unmanned aerial vehicle.
How to Cite
Fault Detection, Monte-Carlo, Highest Density Regions, Nonlinear-Models, Residual Thresholds
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.