Simple Non Regressive Informed Machine Learning Model for Prescriptive Maintenance of Track Circuits in a Subway Environment-Reference-Cited by-同舟云学术

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Simple Non Regressive Informed Machine Learning Model for Prescriptive Maintenance of Track Circuits in a Subway Environment

Published:2022-09-04 Issue: Volume: Page:74-83
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:
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Author:

Garrone Andrea,Minisi Simone,Oneto Luca,Dambra Carlo,Borinato Marco,Sanetti Paolo,Vignola Giulia,Papa Federico,Mazzino Nadia,Anguita Davide

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-16281-7_8

Reference21 articles.

1. Alvarenga, T.A., Cerqueira, A.S., Filho, L.M., Nobrega, R.A., Honorio, L.M., Veloso, H.: Identification and localization of track circuit false occupancy failures based on frequency domain reflectometry. Sensors 20(24), 7259 (2020)

2. Chen, J., Roberts, C., Weston, P.: Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems. Control. Eng. Pract. 16(5), 585–596 (2008)

3. Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)

4. Consilvio, A., et al.: Prescriptive maintenance of railway infrastructure: from data analytics to decision support. In: International Conference on Models and Technologies for Intelligent Transportation Systems (2019)

5. D’Amato, V., Oneto, L., Camurri, A., Anguita, D.: Keep it simple: handcrafting feature and tuning random forests and Xgboost to face the affective movement recognition challenge 2021. In: International Conference on Affective Computing & Intelligent Interaction (2021)

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1. Cognitive Principles for Remote Condition Monitoring Applied to a Rail Pantograph System;Applied Sciences;2024-07-03

2. A data-driven prioritisation framework to mitigate maintenance impact on passengers during metro line operation;European Transport Research Review;2024-01-15

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