A Bayesian assessment for railway track geometry degradation prognostics

Chiach�o, Juan; Chiach�o, Manuel; Prescott, Darren; Andrews, John

Abstract

Advanced PHM techniques have the potential to substantially reduce railway track maintenance costs while increasing safety and availability. However, there is still a significant lack of knowledge and experience in relation to suitable PHM models and algorithms within the context of railway track geometry degradation. This paper proposes a Bayesian model class methodology for prognostics performance assessment whereby different prognostics algorithms can be rigorously assessed and ranked according to their relative probability to predict the future degradation process. The proposed framework is exemplified and tested for a case study about track degradation prognostics using published data about track settlement, taken from a simulated traffic loading experiment carried out at the Nottingham Railway Test Facility.

Citation

Chiachío, J., Chiachío, M., Prescott, D., & Andrews, J. (2018, July). A Bayesian assessment for railway track geometry degradation prognostics. Presented at 4th European Conference of the Prognostics and Health Management Society (PHME 2018)

• Conference Name: 4th European Conference of the Prognostics and Health Management Society (PHME 2018)
• Start Date: Jul 3, 2018
• End Date: Jul 6, 2018
• Acceptance Date: Jun 15, 2018
• Online Publication Date: Jul 1, 2018
• Publication Date: Jul 6, 2018
• Deposit Date: Sep 19, 2018
• Publicly Available Date: Sep 19, 2018
• Book Title: Proceedings of the 4th European Conference of the Prognostics and Health Management Society
• Chapter Number: N/A
• ISBN: N/A
• Public URL: https://nottingham-repository.worktribe.com/output/1090772
• Publisher URL: https://phmpapers.org/index.php/phme/article/view/461
• Contract Date: Sep 19, 2018