Margot F. E. Peters
Suspension defects in passenger trains reduce safety and comfort in rail transport systems. We investigated the feasibility of detecting such defects using dynamic wheel load differences (DWLDs), which are measured from trains in operation using sensors in the track.
We found that DWLD data shows considerable variability but in a consistent manner, and developed a method that purifies the data to improve signal-to-noise. Further, we developed an algorithm that uses the purified DWLD data to detect anomalous events in suspension imbalance, which is indicative of a defect or repair event. Our algorithm further provides a diagnosis of an anomalous event as related to either the primary or the secondary suspension system. We validated our algorithm using a limited set of maintenance records and found a high detection and correct classification rate, although more extended validation is needed with more maintenance records and DWLD data.
In sum, our work indicates a promising avenue of high-frequent, automated detection and diagnosis of suspension defects, which would contribute to efficient, economical and save operation of railway vehicles.
How to Cite
wheel load differences, railways, safety, suspension, anomaly detection, trains
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.