Techniques for predicting railroad track geometry exceedances
US12017691B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 8, 2021 |
| Grant date | Jun 25, 2024 |
| Priority date | — |
| Expiry date | Aug 19, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In example embodiments, techniques are provided for using machine learning to predict railroad track geometry exceedances to enable proactive maintenance. A machine learning model of a rail operational analytics application may be trained to directly output a probability of future railroad track geometry exceedances for each portion of track of a railroad. Training may be performed using all available railroad track data, and the task of selecting which data is relevant to predicting probability of railroad track geometry exceedances may be devolved to the machine learning model. Further, assumptions about the specific railroad and data characteristics may be avoided, providing the machine learning model flexibility, and allowing for dynamic changes in the problem formulation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.