This contribution presents a data-based model that exploits
the power consumed by point engines during blades
movement of railway switches to detect relevant anomalies
in switch behavior. The model incorporates local air
temperature at the time of the measurement to account for
the significant influence of the environmental conditions on
normal switch behavior. Anomaly detection by the model is
validated against alerts triggered by the state-of-the-art
monitoring system POSS®, which is based on switchspecific and manually selected reference curves. The databased model leads to less in number and more reliable alerts
in comparison to the current version of POSS®. Especially
false alerts caused by temperature effects are significantly
reduced. Furthermore, the high sensitivity of the model
proves to be capable of detecting emerging switch failures at
an early stage of development. The detection capabilities of
switch condition (nowcast) and identification of emerging
failures at an early stage (required for failure forecast)
proves that the model is useful for traffic interference
prevention, condition-based predictive maintenance and
switch health enhancement.
How to Cite
Condition-based maintenance technologies, Data-driven and model-based prognostics, Asset health management
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.